MLA-C01 practice questions
AWS · MLA-C01 · 300 questions
Validates associate-level AWS machine learning engineering skills, covering data ingestion and preparation, model development and tuning, deployment and orchestration of ML workflows, and monitoring, maintenance, and security of ML solutions using Amazon SageMaker and related AWS services.
This course contains the use of artificial intelligence.
About the MLA-C01 exam
- Exam fee
- $150 USD
- Time allowed
- 2 hours 10 minutes
- Questions
- 65
- Passing score
- 720 (scale 100-1000)
Exam details published by the vendor, checked 28 August 2026. Vendors change fees and formats without notice — confirm on the vendor's own page before you book.
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Data Preparation for Machine Learning (ML) · 84 questions
- A municipal transfer station lands 80 daily weigh-ticket columns on Amazon S3. Analysts later scan only five columns with Amazon Athena before the set is handed to Amazon SageMaker. Which file layout should they choose?
- A municipal water-taxi kiosk emits nested JSON boarding events that operations staff still open in a text editor. Next quarter the same objects will be scanned as flat columns for a no-show model. When should the team keep JSON, and when should they convert?
- A municipal grain-silo co-op already runs Apache Hive-style tables on Amazon EMR and wants a columnar, type-aware file layout that those jobs already speak. Which format fits that access pattern?
- A community shortwave logger keeps adding fields to show-log records while older files must still be readable. Writers need a compact row format that carries the schema with the data. Which format supports that evolution better than CSV?
- A municipal curling-club attendance team wants to feed an Amazon SageMaker built-in algorithm that expects the RecordIO-wrapped layout rather than raw CSV lines. Which ingest convention should they apply?
- A municipal arboretum sensor desk can either enforce a declared schema as files land or drop whatever bytes arrive and discover columns later. Which pair names those two ingest paths?
- A county plat office dumps every parcel photo and roll row under one Amazon S3 prefix. Training jobs time out listing objects. Which change matches the access pattern without buying a bigger instance?
- A historic sawmill finishes each night’s kiln logs and needs cheap, durable object storage that AWS Glue, SageMaker Data Wrangler, and later training jobs can all read. Where should that batch lake land?
- Three Amazon SageMaker training instances must open the same directory tree of bat-detector spectrogram files with ordinary file-path reads, not object GETs. Which store should they share?
- A county fry-hatchery’s existing on-premises NetApp share already holds labeled fry photos, and the ML team wants those same NFS semantics in AWS without rewriting the read path. Which store should they choose?
- A municipal rail-yard vibration set is tens of tebibytes of sequential files that training jobs will stream repeatedly. Object GET overhead dominates. Which initial storage decision fits that I/O pattern?
- A municipal trolley farebox database lives in Amazon RDS. The ML engineer needs those tables in the S3 lake without a hand-rolled nightly dump script if a managed extract exists. Which approach is correct?
- A community archive-card tap table in Amazon DynamoDB is hot during opening hours. The team needs a point-in-time extract for a hold-prediction model. Which extract path should they prefer?
- A municipal materials-recovery workstation stages local shards before upload; the default general-purpose EBS volume stalls under random reads. Which lever matches that bottleneck?
- A community hop-yard field office in a far Region uploads harvest CSVs into a lake bucket in another Region and watches WAN transfers crawl. Which Domain 1 extract option speeds those long-distance uploads?
- A municipal water-plant still writes lab CSVs to an on-premises NFS export and needs a scheduled, checksummed copy into Amazon S3 for later SageMaker Data Wrangler work. Which in-scope service should they use?
- Municipal tug-dispatch radios emit position pings that must keep per-vessel order and be available to multiple consumers within seconds. Nightly S3 drops miss the freshness bar. Which ingest service fits?
- A municipal sled-hill grooming desk wants click-and-sensor records to land as objects in Amazon S3 every few minutes without the team managing stream shards or consumer applications. Which service fits?
- A municipal aquarium gate needs five-minute rolling visitor counts with state, late events, and windows before those aggregates land for a staffing model. Which ingest path should they use?
- A community credit-union core already publishes ledger-change messages to Apache Kafka and will not rewrite producers this quarter. The ML lake must subscribe to that protocol. What should the team keep as the streaming source?
- A community hoop-house climate model must see new sensor rows within ten minutes, but today’s pipeline is a 2 a.m. Amazon S3 drop. Which ingest change meets that freshness bar?
- Several teams must read the same Amazon S3 lake tables for feature work, and the data engineer wants a catalog-level permission model rather than a thicket of raw bucket policies. Which Domain 1 service pair fits?
- Orchard-harvest CSVs sit in Amazon S3, but SageMaker Data Wrangler and Amazon Athena show no tables. Which missing step should the team take?
- A county harvest-fair needs ticket scans from Amazon S3, vendor payments from Amazon RDS, and volunteer shifts from Amazon DynamoDB as one training table. Which merge path matches the official skill?
- After maple-sugar CSVs reach Amazon S3, a small ML team wants a visual flow to profile and start transforms without standing up Amazon EMR first. Which ingest-into-prep step should they take?
- A municipal canal-ferry operator wants reused numeric features written once and read later by both training and, later, serving. Which Domain 1 ingest target should they load?
- Cold kiln archives are rarely reread; hot daily logs are scanned every training run; a third pile is a sparse wide table. How should the team map those piles, and what must they not do?
- An AWS Glue job and later training listings stall because a kiosk writes millions of 4 KB objects. Which ingest fix matches that scale issue?
- A municipal grain-scale dataset has a few tickets recorded in pounds instead of tons, which pull a linear scaler sideways. What should the team do before scaling or training?
- County forage-plot moisture probes drop overnight readings. Dropping every incomplete row would erase frost nights. Which missing-value strategy is appropriate?
- Community festival volunteer logs leave the role field blank on weekends. Filling those blanks with the weekday mode would invent a role that never happens on Saturday. Which missing-categorical treatment is appropriate?
- A municipal water-taxi farebox extract double-wrote the same tap when a job retried. Training row counts jumped but unique riders did not. Which cleaning step should run before feature builds?
- Combining night-market ticket scans with a many-row vendor catalog on a loose key multiplies rows and fabricates extra purchases. How should the team combine those sources?
- A 12-column berry-harvest table needs histograms, a few imputes, and an exported flow. Standing up Amazon EMR Spark is overkill. Which tool matches that modest SageMaker-bound set?
- A county permit clerk who does not live in SageMaker Studio still needs a visual recipe to clean permit CSVs in the lake. Which official no-code prep tool fits that path?
- Nightly merges of three 200-million-row sources need a repeatable Spark job, job bookmarks, and a catalog update. Which service should run that transform?
- A historic lime-works transform needs a specific Spark runtime and native libraries that the team already packages on Amazon EMR, not the Glue-only path. Which compute should they choose?
- Each trail-register ping needs a unit conversion and a field drop before it lands. The payload is tiny and stateless. Which official lightweight transform should run on the stream?
- Gondola boarding events need sessionization across minutes of out-of-order records before features are written. A single AWS Lambda timeout is the wrong ceiling. Which transform path fits?
- Aquarium gate-wait minutes are roughly bell-shaped; another column is a hard 0–100 saturation percent. How should those two numeric columns be scaled?
- Municipal bulk-trash surcharge dollars run from pocket change to a few extreme commercial hauls. A linear model treats the tail as the whole story. Which transform should they apply to that positive amount first?
- Water-taxi rider age is messy at the edges, but planning only needs child, adult, and senior bands. Which feature step matches that need?
- A single gondola departureat string hides hour-of-day and weekday that a no-show model needs. Which feature-engineering technique should they apply?
- Municipal tug vessel class has four unordered labels. Feeding those strings into a linear trainer is wrong. Which encoding should they apply?
- Forage-plot soil-grade is poor, fair, good, excellent. Integer ranks preserve that order. Using the same integers on unordered berth names would invent a false rank. When is label encoding correct?
- Municipal trolley stop-ID has hundreds of values; one-hot would explode width. Which official alternative encoding should they choose?
- Festival volunteer incident notes need to become tokens before any bag-of-words or embedding step. Which Domain 1 encoding technique is that, and what should the item not become?
- A tree ensemble on the same bulk-trash table is insensitive to column magnitude, but a k-nearest or linear path on that table is not. When should the team scale?
- A notebook fits a scaler on the entire hop-harvest file, then splits. Validation rows have already leaked into the transform. What did the team do wrong?
- Nightly lock-ferry training reads a historical feature snapshot; a later low-latency lookup needs the current row. Which two SageMaker Feature Store access patterns is that?
- Two gondola notebooks independently recompute mean wait over 7 days and drift from each other. Which Feature Store practice keeps train and serve aligned?
- Orchard botanists must tag leaf-damage classes on a private workforce with a labeling UI and audit trail. Which official labeling service should they use?
- A one-off batch of 8,000 short choir-rehearsal clips needs human tags and the agency has no private labelers. Which official public workforce should they use?
- A historic brick-kiln has ten years of unlabeled traces and a smaller Ground Truth-tagged defect set. Which pile is supervised training data?
- After humans tag a seed batch of marina berth photos, the team wants the service to propose labels on the rest for human confirmation. Which Ground Truth pattern is that?
- A night-market ticket parse is usually fine, but low-confidence rows should pause for a person before they enter the training table. Which in-scope service routes that review?
- Failed potash-kiln batches are 2 percent of rows. Accuracy looks fine if the model always predicts pass. Which official pre-training metric should the team compute first?
- Priority inspection labels appear far more often for one tug harbor than another even when traffic volume is similar. Which official pre-training metric surfaces that gap?
- Festival volunteer incident notes are almost all routine; the hazard class is a handful of paragraphs. Which data-prep problem is that?
- An orchard leaf-photo set has thousands of healthy leaves and a few blight shots. Training on the raw folders will ignore blight. Which data-prep problem is that?
- A historic brick-kiln’s 2 percent defect class needs more presence in the training slice. Which official strategy should they apply, and where?
- There are too few vineyard blight photos to undersample healthy ones without starving the set. Which official CI strategy can supply more minority examples?
- Transfer-station tickets were only recorded on weekday mornings, so weekend commercial haulers never appear. Which pre-training bias is that?
- One grain-scale weighbridge reads 8 percent heavy, and one botanist tags damage much more strictly than peers. Which bias is that, and what is it not?
- Before any trainer starts, the team needs CI, DPL, and related pre-training metrics on the berry-harvest table. Which official AWS tool should they run?
- Null rates on forage-plot moisture and impossible negative weights should fail the nightly job before a training dataset is published. Which official lake-scale validator can block that publish?
- A clerk wants an interactive quality look at a 20 MB permit file; the lake job needs a scheduled ruleset on 200 million rows. Which official tool fits which moment?
- Independent grain-scale tickets with no time leak need a held-out test slice and a validation slice. Which official prep split should they create?
- Daily trolley ridership rows must not let next month’s fares appear in this month’s training fold. Which split should they use, and what is the trap?
- Plat files were written district-by-district, so the last 20 percent is only one district. Splitting in file order creates a biased holdout. Which official prep step should they run first?
- A random split on a 2 percent brick-kiln defect table can put almost no defects in validation. Which split keeps the rare class in each slice?
- The vineyard blight folder is tiny. Simple crops and flips of existing blight photos enlarge that class without new field visits. Which official prep step is that?
- A resolutionhours field is filled only after a defect is closed, but it sits in the training table used to predict whether a defect will occur. What should the team do with that column?
- The berry-harvest lake holds grower identifiers and must sit encrypted under a customer-managed key before any SageMaker job reads it. Which Domain 1 control is that?
- Hop-yard field offices still upload over plain HTTP to a public bucket URL. Which integrity control belongs on the way into the lake?
- Water-taxi rider names and card numbers are still in the transform output that data scientists pull. Where should those columns be handled?
- A credit-union ML table still needs a stable customer key for joins but must not carry the raw account number. Which official technique should they apply?
- A clinic’s visit-risk table includes protected health information and must remain in a single approved Region. Copying it to a cheaper distant Region for just prep is not allowed. Which handling is correct?
- Nobody is sure whether old S3 prefixes still hold national IDs. Which in-scope service should inventory that PII before the objects become features?
- Public tide tables, internal ferry logs, and confidential manifest notes sit in the same bucket prefix. What should drive encryption, masking, and who may ingest each pile?
- Coastal lidar tiles already sit on Amazon EFS. Recopying that tree onto each SageMaker instance root volume would move several terabytes before training starts. How should the training resource read the tiles?
- A high-throughput Amazon FSx for Lustre file system already holds sequential rolling-mill acoustic traces. The team needs the SageMaker trainer to read that volume instead of restaging the traces. What should they configure?
- A 4 TB S3 prefix of orchard drone frames is read sequentially. The algorithm does not need every object on local disk before the first epoch, and a full copy delays the job. Which SageMaker channel input mode fits?
- A city bus-ridership table still fails Glue Data Quality checks, and SageMaker Clarify has not reported pre-training bias. The lead wants to launch the SageMaker training job tonight and clean the table later. What should the team do first?
ML Model Development · 78 questions
- A marina has forty hull photos tagged barnacle or clean and wants a custom SageMaker image trainer this week. What should the ML engineer decide first?
- The same orchard crate table can predict whether a crate will pass inspection or the continuous brix reading in degrees. What modeling choice comes first?
- Ferry-terminal logs have hour, weather, and passenger counts, but nobody tagged busy versus quiet. Staff want natural groups of similar terminals. Which approach fits?
- Several related ferry routes log hourly ridership that shares weather and school-holiday patterns. Treating each hour as an independent tabular row ignores that related series. Which SageMaker algorithm fits?
- A water-treatment plant has a long turbidity trace and a few odd spikes, but no labeled incident rows. Staff want those spikes flagged. Which SageMaker algorithm fits?
- A library-board work session must walk through a late-fee model with a signed weight on each factor. Which SageMaker built-in fits that interpretability constraint?
- Grain-elevator no-shows jump only when harvest week, rain, and contract type collide. Nobody is asking for readable coefficients this week. Which SageMaker algorithm fits?
- A tool library wants borrowers who checked item A also checked item B from a mostly empty borrower-by-item table. Which SageMaker algorithm fits that sparse interaction matrix?
- County vineyard clerks want a new block scored as like these five known blocks, not a 400-tree ensemble. The labeled set is small and local similarity is the business story. Which SageMaker algorithm fits?
- Lock-chamber fill time is a modest tabular regression with no custom loss. One engineer wants a blank PyTorch script, another wants a JumpStart foundation-model template, and a third wants a built-in estimator. Which path fits?
- Harbor-pilot log notes need a text classifier, and SageMaker JumpStart already lists a pretrained text-classification template. No custom loss is required. Which path fits?
- A county-fair desk wants short vendor blurbs from bullet notes and has a couple of hundred past blurbs, not a million-token corpus. The team does not want to own training infrastructure. Which approach fits?
- Trailhead closure notices must go out in English and Spanish tonight. The desk has no parallel corpus and no week to train a sequence-to-sequence model. Which service fits?
- A town-hall hearing recording must become text before any later model looks at speaker comments. The team will not build a speech recognizer. Which service fits?
- Pier camera stills only need a log of whether a person, vehicle, or animal is in the frame. There is no labeled custom-species folder and no pixel-level mask requirement. Which approach fits?
- Concession-stand review notes need a polarity tag on a few thousand lines. A week of GPU training would exceed the parks budget; an on-demand AI or foundation-model call would not. Which constraint should decide the model?
- Irrigation-district growers will challenge any black-box model that sets the recommended start date. A deeper network might score slightly better on a holdout. What should decide the algorithm?
- Visitor-center comment cards need sentiment and key phrases, and nobody has annotated a training file. A BlazingText job would require labels the desk does not have. Which service fits?
- A municipal kayak-share wants next-dock or pass add-on suggestions for members and does not want to own a factorization-machine training loop. The need is a standard personalization workload. Which service fits?
- Harbor inspectors already tagged eight thousand dock-plank photos sound versus rot. They want a SageMaker trainer they control, not a general-purpose detect-labels API. Which algorithm fits?
- Marina slip-rental refund claims look like a classic application-fraud queue. The clerk team wants a managed fraud service, not a home-grown XGBoost feature shop. Which service fits?
- An ice rink already has a written rule: if brine temperature rises above 22 F, add coolant. Someone filed a ticket to train a model on three winters of that same rule firing. What should the ML engineer do?
- A harbor-tug delay trainer runs 10 epochs with batch size 64 on 6,400 rows. What is one epoch, and how many parameter updates occur in each epoch?
- A greenhouse irradiance trainer dies with an out-of-memory error after the batch size is raised to speed the job. The next guess is to add epochs. What should change instead?
- A beach-cleanup hour model is scheduled for 80 epochs, and validation loss stopped improving at epoch 14. The job is still burning instance hours. What should cut that time?
- Canal-camera barge-count images take fourteen hours on one instance. The code already uses a supported framework and the set is large enough to shard. Which official method cuts that wall-clock time?
- A lighthouse-foghorn classifier has a few hundred rows. An engineer copies a multi-GPU distributed recipe from a larger job and watches communication overhead exceed compute. What should they do?
- A grain-silo vibration trainer can pause and resume from checkpoints, and the team wants a lower training bill. The setting lives on the training job, not on a production endpoint purchase plan. What should they enable?
- A lock-tender narrative classifier memorizes clerk names that appear in the training notes and then fails on new crews. The architecture is a neural net, not a linear model. Which regularization technique fits?
- A bookmobile arrival model grows huge coefficients on a couple of rare street names and then swings wildly on the next week's routes. Which regularization technique fits?
- A municipal compost-bin table has dozens of weak optional columns, and the trainer should be allowed to drive many of those weights to zero. Which regularization produces that sparsity?
- A pier-vendor XGBoost job with a handful of trees underfits; the next run with a huge tree count memorizes last season's sales. Which hyperparameter is that lever?
- A lock-gate sensor network keeps adding hidden layers on the same small labeled set, and validation error gets worse, not better. What is happening?
- A hatchery incubator-temperature trainer shows a loss that explodes after a few steps. Someone's first fix is to run more epochs. What should change first?
- A grain-elevator price XGBoost job needs a search over eta and maxdepth instead of one hand-edited training job. Which SageMaker path launches that search?
- A ferry no-show tuner can afford only a small number of sequential training jobs, so it should learn from earlier trials. A second team has a wide space and many cheap parallel jobs. Which AMT strategies fit?
- A band-shell tuner lists several continuous hyperparameters with fine ranges. An engineer wants every combination on a full grid. What is the cost risk?
- A marina slip-injury flag is a rare class, and the HyperparameterTuner is maximizing training accuracy. The rare class never moves the score. What should the tuner optimize instead?
- A water-plant chlorine tuning job launches many hopeless hyperparameter combos that run to the last epoch. How can SageMaker AMT cut that waste?
- A harbor-radar team needs a custom attention loop that no SageMaker built-in ships. They already write PyTorch and do not need a new AWS service. Which training path fits?
- A pier-weather spectrogram model already lives in TensorFlow and must keep that graph. Rewriting it as Linear Learner would drop the architecture. Which SageMaker path fits?
- Irrigation-district staff have a working scikit-learn pipeline and want it trained as a managed SageMaker job, not rewritten in XGBoost and not handed a blank CUDA kernel. Which path fits?
- Dock inspectors have a modest labeled plank-photo set and a JumpStart vision model that already knows generic image features. Random-initialized training would waste that head start. What should they do?
- A municipal archives desk wants memorial blurbs in a house voice and has a private corpus. They do not want to train a giant sequence model from random weights. Which approach fits?
- A lock-chamber ridership network posts a tiny training error and a large validation error. The first proposed fix is more epochs on the same architecture. What should they do instead?
- A ferry-delay model is weak on both the training slice and the validation slice. Someone adds heavier dropout and a stronger L2 penalty. What should change instead?
- A JumpStart text model fine-tuned hard on lock-tender notes now fails ordinary English prompts it handled last week. The new domain overwrote the old weights. What happened, and what should they try?
- A pier-meter table has a couple of hundred sparse columns and only a few hundred rows. The trainer memorizes rare meter IDs. This is a refine-the-model choice, not a Domain 1 encoding recipe. What should they do?
- A band-shell no-show Linear Learner and an XGBoost each miss different nights. Averaging or voting their scores beats either model alone. Which refine step is that?
- Kayak-share demand already has two decent base trainers. A third model is trained on those models' scores rather than on a simple average. Which technique is that?
- A pier-vendor sales model is built by adding trees that correct the previous trees' residuals. Which combination style is that, and which SageMaker built-in usually does it?
- A harbor-current network artifact is larger than the team will later accept. Official refine skills name pruning and compression. Quantizing and restudying accuracy is out of exam scope. How should they shrink the model?
- A greenhouse-shade trainer writes weights as 64-bit floats and the artifact balloons. Which official size lever should they use?
- A trail-crew network grew after the team added layers, widened hidden units, and kept every raw ID feature. They blame the lake bucket. What actually drives model size?
- A grain-elevator team overwrites a single best.tar.gz each night and cannot say which artifact produced last Thursday's prices. Repeatability and audit are required. Which SageMaker store fits?
- An orchard consultant trained a model on a laptop and handed over an artifact. The county wants that model managed in SageMaker AI without retraining it from zero. What should they do?
- A marina 'will this slip dispute escalate' classifier shows many false alarms and a smaller number of missed true escalations. The counts sit in a two-by-two table. What evaluation artifact is that, and what do the cells mean?
- A lock-and-dam 'needs a tow assist' flag treats a missed true case as far worse than an extra false launch. Accuracy is not the decision. Which metric should they optimize?
- Municipal boat-ramp permit reviews punish both a false grant and a false denial. Neither precision nor recall alone is enough. Which metric balances both?
- Water-plant 'overflow event' rows are about one percent of the week. A model that always predicts no overflow posts 99 percent accuracy and is useless. What is wrong with that headline metric?
- Lock-chamber fill gallons are a numeric target. Someone reports F1 after splitting the gallons column at its median. Which metric fits the continuous target?
- A ferry no-show score will be cut at different operating points depending on how full the lot is. A single 0.5 accuracy is not enough. Which evaluation pair compares ranking quality across thresholds?
- A dock-slip type classifier keeps mixing two neighboring slip codes. Which evaluation display makes that pair obvious?
- A pier-concert attendance XGBoost is called a win before anyone scores predict last week's headcount. There is no baseline. What should they do first?
- An ice-rink model shows training AUC near perfect and validation AUC far lower. This is the evaluation diagnosis, not the regularization fix. What does that gap mean?
- A brine-temperature regressor posts a large RMSE on the training slice and a similarly large RMSE on validation, and both lose to a seasonal mean baseline. What does that pattern mean?
- A compost-bin table is small enough that one lucky holdout would flatter a model. The team needs a rotation of folds during analysis, not a Domain 1 one-time split. Which evaluation technique fits?
- A tug-delay model's fold scores jump all over, while a second, too-simple model is equally weak on every fold. How should those evaluation patterns be read?
- A library occupancy model is in front of the board, and staff need which features drove a full score last Saturday. This is post-training explanation, not a Domain 1 pre-training bias report. Which service fits?
- A kayak-share demand model may score one zip code systematically worse after training. The official Domain 2 Clarify use is insights into the trained model, not the Domain 1 CI/DPL data report. What should they run?
- Bookmobile arrival errors are consistently larger for one neighborhood even though the overall RMSE looks fine. What should the analysis notice?
- A hatchery-incubator network's training loss oscillates and never settles, while instance CPU is idle and S3 throughput is fine. Someone wants a larger training instance. What is the real problem?
- The same oscillating hatchery job needs tensor-level evidence of vanishing or exploding gradients, not another guess at the learning rate. Which SageMaker capability fits?
- A band-shell recommender's new candidate should see a copy of live traffic without replacing the current production variant. The Domain 2 job is to compare those two performances. What should they compare?
- A grain-silo vibration tuner buys a tiny AUC bump after twelve extra GPU hours. The works-budget owner asks whether that bump is worth it. What should the analysis weigh?
- Grain-elevator price trials cannot be replayed: no recorded hyperparameters, no seed, artifacts overwritten. How should those experiments become reproducible on AWS?
- Canal-lock minutes until next lockage is a regression target, but the write-up quotes AUC. What is wrong with that analysis?
- A lock-tender transport-now classifier defaults to a 0.5 cutoff even though the confusion matrix shows that cutoff floods the bay with false launches. How should they choose the operating point?
Deployment and Orchestration of ML Workflows · 66 questions
- A marina slip-application clerk UI must return a risk flag in well under a second, all day, with steady office-hour traffic. Which SageMaker serving mode fits?
- A county-fair pie-score model is invoked a few dozen times on fair weekend and then sits idle for months. The board will not pay for an instance that stays up. A cold start of a few seconds is acceptable. Which serving mode fits?
- Pier cameras drop multi-hundred-megabyte clips that take many minutes to score. Clerks can wait for a queued result; they cannot block a synchronous HTTP call for an hour. Which serving mode fits?
- Once a week the compost office drops a single large CSV of well readings and wants a scored file back in the same bucket. Nobody will call an API per row, and no endpoint should sit around between runs. Which serving mode fits?
- Ferry planners need a bunching-risk score for every stop by 06:00 from last night's AVL dump. The SLA is a completed file, not a sub-second UI call. Which deployment strategy fits?
- A hatchery lab uploads one long raw-instrument file per sample. Scoring plus preprocessing overshoots the real-time timeout, and the file is too large for a serverless payload, but a same-morning queued result is fine. Which mode fits?
- A moth-trap count model is hit in short evening bursts, then silent until the next dusk. Finance flagged last month's always-on real-time bill. A short cold start is acceptable for the field app. Which tradeoff should they make?
- A dock-camera species tagger is a large vision network, and CPU hosts miss the ranger-app latency budget. A second, tiny tabular lot-full model on the same campus does not need a GPU. How should inference compute be chosen?
- Parks IT wants the staging reservation-no-show endpoint to mirror production instance counts so it feels real, even though staging traffic is a handful of synthetic calls a day. How should test compute be provisioned?
- A water-plant turbidity model must sit on a CPU family the agency already standardized, and each invoke ships a wide feature vector from an in-VPC store. The constraint is host generation and NIC bandwidth, not use a GPU because it is ML. What should select the inference host?
- The harbor-tug wait model is a supported scikit-learn or XGBoost-class artifact. A contractor wants to spend a sprint writing a custom image for control. Which inference container should they use?
- The lock-chamber occupancy scorer depends on a niche hydraulics library that no SageMaker provided inference image ships. The serving contract is still a SageMaker endpoint. Which container path fits?
- Each county boat-ramp has its own contamination model with the same framework and roughly the same size. Most ramps are quiet; only a few are busy at once. One endpoint per ramp would waste hosts. Which endpoint pattern fits?
- Visitor-center comment scoring always runs a tokenizer/featurizer container and then a model container on the same request. The agency wants one endpoint, not two hops the app team must glue together. Which deployment fits?
- The ice-rink routing model is invoked continuously during storms. A teammate proposes stuffing it onto the existing multi-model endpoint beside twenty rarely used precinct models. What should they use instead?
- A new compost-intake scorer has no existing container platform. The county already trains in SageMaker AI and wants a managed HTTPS endpoint, not a new cluster. Which deployment target fits?
- The bookmobile due-tonight ranker is a tiny serialized model, invoked from an existing Lambda-backed API, a few thousand times a day, well inside Lambda size and timeout limits. Standing up SageMaker hosting would be new platform work. Which target fits?
- Parks already serves three internal APIs on Amazon ECS. They want the trail-washout severity model packaged as one more service on that cluster, with the same deploy path, not a new SageMaker endpoint estate. Which target fits?
- The ferry operations team already runs Amazon EKS for internal tools and requires the new delay scorer to land as a Kubernetes Deployment next to those services. Which target fits?
- Pier kiosks run a small local board with a named target architecture and cannot call the VPC during weekend outages. The trained artifact must be compiled to that device, not hosted as a cloud endpoint the kiosk cannot reach. Which capability fits?
- One county team already runs dozens of Airflow DAGs on Amazon MWAA. A greenfield team has only SageMaker training and processing steps and no Airflow estate. Both need an orchestrator for the deployable workflow. How should each team choose?
- Finance will not fund GPU real-time hosts so rangers can click score this photo instantly. Overnight batch scoring of the day's dock-camera dump meets the posted-alert SLA at a fraction of the cost. Which serving choice fits?
- After a bad tug-wait model went live, ops needs to put the previous endpoint configuration back in minutes. The artifacts are already registered. What is the Domain 3 gap?
- Two approved dock-inspection models must share live traffic on one real-time endpoint from the moment of deploy (10 / 90 split). Ops is shifting invoke traffic, not reading a shadow copy and not running a weeks-long production A/B monitor. What should they configure?
- The pier-advisory serverless endpoint is cheap overnight but clerks hit a cold-start wall at 08:00 opening. Leadership will pay for a small reserved slice during business hours only. Which provisioning choice is that?
- Marina-permit traffic is smooth most days, spikes when a storm-repair window opens, and is predictably dead on Sundays. The team must pick a scaling-policy shape, not an instance family. Which policies match those shapes?
- Every compost-scoring environment was clicked together in the console and now drifts. The requirement is a repeatable template for the model, endpoint configuration, and endpoint. Which official IaC service fits?
- The ferry team already authors AWS CDK apps in Python or TypeScript for other services and wants the bunching endpoint expressed as constructs in that same app, not a new YAML dialect. Which IaC path fits?
- Two equally valid templates would stand up the lock-chamber endpoint. One group lives in reviewed CloudFormation YAML; the other wants CDK because they already review Python PRs. What should decide the IaC choice?
- Networking already owns a CloudFormation stack that exports subnet and security-group IDs. The ML stack must consume those exports so the new endpoint lands in the existing VPC without copying IDs into chat. What should they automate?
- The custom lock-chamber hydraulics serving image is built in CI and must be pulled by SageMaker (or ECS/EKS) in each account. Someone proposed leaving tarballs in a personal S3 prefix. Where should the image live?
- After the lock-chamber team chose a custom container, they still need the scripted path: image in ECR, SageMaker model image URI set, endpoint created. Which BYOC steps fit?
- Parks already picked Amazon ECS as the hosting target. The remaining work is a task definition plus service that runs the serving container from ECR, not a SageMaker Model.deploy call. What should they script?
- Ferry operations already picked Amazon EKS. The serving image must run as a Kubernetes Deployment on that cluster, with the image coming from ECR. What should they script?
- The brine-plant model must not be reachable on the public internet. Private subnets and a security group already exist; the endpoint just is not using them. What should they configure?
- An ice-rink engineer has a trained Model (or estimator) in a notebook and needs the snippet-level call that creates the real-time endpoint, not a CloudFormation rewrite and not a from-scratch Flask server. Which path fits?
- The marina-permit real-time variant is even on CPU, but each host is drowning in invoke count at noon. The scaling signal should be traffic per host, not a guess at CPU. Which auto scaling metric fits?
- The tug-wait hosts show high CPU while invoke counts still look modest, because each call does heavy featurization. Scaling on invoke count alone never adds capacity in time. Which auto scaling metric fits?
- Ice-rink scoring still returns 200s, but p99 model latency crosses the dispatch budget before CPU looks high. Ops wants capacity to follow latency, not a vanity CPU chart. Which auto scaling metric fits?
- Pier-festival ticket-risk invokes are near zero except 10:00–20:00 on event days, which are on a published calendar. Scaling only on live demand always lags the opening rush. Which auto scaling approach fits?
- Boat-ramp contamination traffic follows no weekly clock. A viral news story can 10x invokes on a Tuesday. A Sunday scale-down schedule would strand weekday spikes. Which auto scaling approach fits?
- The existing ice-rink mobile API is already AWS Lambda. The new no-show model lives on a SageMaker endpoint. Lambda should validate the payload and call the endpoint rather than exposing the endpoint to the phone app. How should Lambda be used?
- A burst of overnight batch-style EC2 scoring for dock-camera dumps can die and retry. The scripted fleet should add Amazon EC2 Spot Instances for that elastic slice and keep a small On-Demand core. Which capacity move is that?
- Fear of a Saturday festival surge led parks to pin eight real-time hosts all month. Average utilization is tiny. What is the maintainable fix?
- Someone turned on a CloudWatch alarm but never registered the SageMaker variant with Application Auto Scaling, so instance count never moves. What step is missing?
- A well-meaning intern changed the staging endpoint instance type in the console. The CloudFormation or CDK stack no longer matches reality, and the next deploy rolled the change back. How should hosting changes be made?
- The compost-intake model is promoted by hand: download the artifact, click deploy. Leadership wants a Source, Build, Deploy pipeline that releases what Git says, not what is on someone's laptop. Which service shapes that pipeline?
- The pipeline's Build stage must run unit tests and build or push the inference image. Someone tried to put that work in a deploy-only click. Which service is the build and test workhorse?
- After CodeBuild succeeds, the county still copies the image tag into the console. They need the deploy tool that rolls the new revision onto the target with a defined strategy. Which service is that action?
- CodePipeline is red on Build. Logs show the buildspec path is wrong and the expected artifact name does not match what Deploy is looking for. What should they troubleshoot?
- Ferry models go to staging when someone emails a zip. The official source of truth should be a Git repository, and a commit or merge should create a pipeline execution. What should start the pipeline?
- One team wants long-lived develop and release branches before production deploy. Another wants merge-to-main as the production trigger. Both must map onto CodePipeline. Which branching structures are those?
- The Git repo exists and the CodePipeline exists, but nothing runs on push. What piece is missing?
- The current pipeline only lints Python and then deploys whatever tar.gz is sitting in a shared bucket. A new commit never proves the model is still fit to ship. What CI/CD principle is missing for ML?
- The next dock-inspection model must stand up beside the live fleet and flip traffic only when the new environment is healthy, with a clean path back to the old environment. Which release strategy fits?
- Ops will not flip 100 percent of ice-rink dispatch invokes to a new revision. They want a small slice of live traffic first, then more only if that slice stays healthy. Which strategy fits?
- Leadership wants production traffic moved to the new tug-wait revision in equal steps every few minutes, not a tiny canary and not a single cutover. Which strategy fits?
- Five minutes after a canary, error rate on the new variant spikes. The pipeline must automatically return traffic to the prior revision instead of paging a human to click around. What should they configure?
- When a new labeled parcel lands in the S3 prefix, or on a cron, nobody should open Studio to click Train. Which service should start the SageMaker training or batch inference job?
- Processing, training, a metric condition, and registration are still run as four notebook cells. The team needs a repeatable DAG that builds the model when invoked. Which service automates that build?
- A merge to main should start the SageMaker pipeline (or a small AWS Step Functions deploy machine) and then update the endpoint. Today those are two bookmarks on a wiki. Which in-scope services should invoke that build and deploy?
- Featurizer helpers and inference serialization broke twice last quarter and were only caught in production. The Build stage has no unit-test step. What should the CI stage add?
- Unit tests pass, but the staging endpoint rejects the real request schema the clerk UI sends. The pipeline ships to production anyway. What test is missing?
- A green pipeline never exercised the path from the S3 drop through the orchestrated job to a scored output the downstream report reads. A silent contract break shipped last month. What should CI/CD add?
- New weigh-ticket labels arrive every Monday. Retrain is a calendar reminder. Official 3.3 skill is a mechanism that retrains when the trigger fires, then proceeds only if the evaluation gate passes. What should they build?
- The ML pipeline assumes a curated prefix that a person loads by hand. Official 3.3 knowledge includes automating data ingestion with the same orchestration so training or inference jobs see fresh data without a human copy. What should they add?
ML Solution Monitoring, Maintenance, and Security · 72 questions
- A lock-chamber wait endpoint is live. Feature histograms look different from last season, but nobody has labeled true wait yet. Which SageMaker Model Monitor type can run on inputs alone, and which one must wait for outcomes?
- A compost-demand endpoint has been scoring for two weeks. The team creates a Model Monitor schedule and gets empty runs. Capture of request and response payloads was never turned on. What is the prerequisite?
- An ice-rink occupancy model is in production. Staff want alerts when something looks off, but they never computed baseline statistics or constraints. They are comparing live captures to last night's captures only. What should they build first?
- Water-plant turbidity features that were always populated now arrive 40 percent null, and one sensor column flipped from numeric to a status string. Invoke count and latency look fine. Which monitor detects that?
- Kayak-share rebalance scores are still 200s, but dispatchers say predicted needs van is wrong all week. Captured predictions sit in S3; the true van-needed labels land a day later in another prefix and were never joined. What should they run?
- A boat-ramp permit model passed a pre-training Clarify check. After a new marina joined, a protected-group facet's live selection rate left the allowed band. This is a production-bias schedule, not a one-off training report. What should they run?
- A harbor-ticket router still meets its F1 gate, but the SHAP ranking that used to lead with category and hour now leads with a rarely used zip field. Leadership wants an alert on attribution order, not another accuracy job. Which monitor fits?
- Dock-plank photos still look like last year's training set, but what counts as must-replace this week changed after a new ordinance. A data-quality monitor stays green while business outcomes rot. What kind of drift is that?
- Pier-vendor no-show features have shifted after a ferry schedule change. The team already used Clarify at train time to explain coefficients. They now need a recurring check that the live feature and prediction distributions moved. Which capability fits?
- Pier-light-outage scoring runs all night. A one-off notebook comparison was run after a complaint and then forgotten. Official Model Monitor skill includes a repeating schedule. What should they create?
- Compost-plot demand is scored once a week with SageMaker batch transform. There is no real-time endpoint. Staff assume monitoring is an endpoint-only product. Can Model Monitor still run?
- A lock-chamber fog-risk model writes a nested payload the prebuilt monitor cannot constrain. The team does not need a new AWS service; they need custom analysis inside the Model Monitor job. Which container choice fits?
- Ice-rink intake monitors write constraintviolations objects to S3. Nobody looks in the prefix until a Friday incident. Official flow ends in Amazon CloudWatch notifications. What should they add?
- Ferry-depot delay models currently retrain every Monday because that is when the pipeline hook was built. A data-quality monitor has been red for ten days while the calendar job is still five days away. Ops must choose the trigger, not redesign the pipeline. Which trigger fits?
- Two approved bookmobile hold-wait models already share live traffic. The Domain 3 weight job is done. For two weeks ops must compare live business and model metrics on both variants and keep both serving customers, not a shadow copy and not a canary bake. What is that window?
- Pier-meter occupancy variants ran 70 / 30 for fourteen days. Error rate and revenue-per-space are now in CloudWatch. Leadership wants a documented winner before anyone changes weights again. What should ops do?
- Marina inventory invokes suddenly return empty bodies with 200s, and the capture prefix stops growing. Model quality reports are stale, not red. What kind of problem is that?
- A lock-sensor Glue job that builds the hourly feature file started writing zero-row outputs after a schema change. The endpoint is healthy; scores are just defaults on empty input. What should they monitor?
- Pier-attendance hosts sit at 20 percent CPU with clean 200s, so the on-call treats the system as healthy. Ticket-office outcomes have drifted for a month. Official 4.1 knowledge includes ML Lens monitoring principles. What is missing?
- Harbor ticket-risk schedules finish Completed every night. Staff never open the statistics, constraints, or constraintviolations objects, then claim monitoring is on. What should they do?
- A land-records vault hosts twenty rarely used classifiers on one SageMaker multi-model endpoint. Staff want one Model Monitor schedule that reports drift for each classifier. What should they do?
- A licensing desk captures hourly wait-time scores all day. True wait times are keyed the next morning. A model-quality monitor that runs at midnight always fails or scores an empty label set. What should they do?
- A ferry-slot scoring endpoint “feels slow” at the ticket window. Nobody has opened Amazon CloudWatch. What is the first infrastructure-health check?
- After a container regression, a snow-plow crew scoring endpoint started returning 5XX. Model Monitor baselines are still green because few successful payloads were captured. What should operations alarm on?
- Culvert-rating scoring has a 400 ms p99 latency budget. Average ModelLatency looks fine, but p99 stayed over budget for an hour and nobody was paged. What should they configure?
- A tree-canopy pest endpoint fails for one payload shape. Metrics show a small error bump, but the team has no log group and cannot see the exception text. What should they enable?
- Three days of container logs sit in a CloudWatch log group. Operations needs the count of timeout messages by hour, not a manual scroll. What should they use?
- A meter-space wait scorer already runs on AWS Lambda. Cold starts and memory max-out are the complaint, and there is no SageMaker host to chart. What should they use?
- A business-license kiosk calls Amazon API Gateway, then AWS Lambda, then a SageMaker endpoint. Users see about 2-second waits. Each hop’s own chart looks acceptable. What should they use to find which hop owns the latency?
- Trail-crew dispatch opens six separate CloudWatch pages every morning to check utilization, latency, errors, and invocations. What should they build?
- Recreation finance wants a weekly, shareable view of endpoint cost and invoke volume by trail district, joined to a tagging export. A CloudWatch metrics widget is too narrow and dies with the console session. What should they use?
- Someone removed a production variant from a marina scoring endpoint last Tuesday. CloudWatch has host metrics, not who called UpdateEndpoint. What should they create?
- When a clerk-approved model is registered, retraining should start from that event, not from a help-desk ticket. The pipeline already exists. What should they wire?
- A compost-route training job flipped to Failed at 02:00. The page came from someone refreshing the console. How should infrastructure failures notify the team?
- A bus-bunching predictor already meets its model-quality gate. Leadership still asks whether the platform was healthy: how busy the hosts were, how many scores per minute, and whether the endpoint stayed up all weekend. Which KPIs answer that?
- The only real-time host for ice-rink wait scoring died during a patch. InstanceCount was pinned at 1 for cost, so availability hit zero. What operational requirement did they skip?
- A dock-camera slip classifier sits on ml.g4dn.xlarge because “vision uses GPU.” Nobody has benchmarked families or shapes for cost and latency. What should they run?
- An EC2 fleet that runs nightly feature builds for a landfill model is about twice the vCPU it uses. Inference Recommender does not see those boxes. What should they use?
- A wide tabular parcel-appraisal model pages heavily on a compute-optimized host. CPU is not saturated. What should they change?
- An address-line matcher was parked on a GPU instance “in case vision is added later.” GPU utilization is near zero and the bill is not. Recommendation jobs already show a CPU family is enough. What should they do?
- A ferry-gate scoring endpoint is over its p99 budget. ModelLatency is about 40 ms, and OverheadLatency dominates. Staff want a larger instance. What should they do first?
- A trail-permit endpoint is slammed with invokes, but DesiredInstanceCount never moves. Target tracking was already configured. What should operations check?
- Auto scaling wants six ml.c5.xlarge hosts for a snow-desk scorer. The account quota is four. CloudWatch shows failed scale activities, not a bad model. What is the problem?
- A meter-space wait scorer already runs on AWS Lambda. After it is warm it meets latency, but the first call after idle nights misses the SLA. What ops control should they use?
- Four park districts share one SageMaker account. Cost Explorer shows a single unallocated endpoint bill. What must they apply before any cost tool can split that spend?
- The monthly SageMaker bill doubled. Tags are already on the resources, but nobody knows whether a forgotten training job, a 24×7 GPU endpoint, or data-capture storage drove the increase. What should they use?
- A compost-yard team will not watch Cost Explorer every day. They need a threshold alert before the month closes. What should they create?
- Several municipal endpoints sit idle on nights and weekends. Staff want a cost and utilization finding they did not have to build by hand. What should they consult?
- A 24×7 scoring fleet and a kill-and-retry nightly feature fleet are both paying On-Demand. How should they match purchasing options?
- Finance needs the official invoice-level breakdown for the ML account (credits, Savings Plan coverage, unused reservations), not only a Cost Explorer chart an engineer bookmarked. What should they use?
- Every intern role can run s3: on the bucket that holds the production model.tar.gz and captured inference payloads. What should they do?
- A harbor surveyor’s IAM user is attached as the role the training job assumes. When that user is deactivated, jobs fail; while it is active, the job inherits console-admin rights. What should they use?
- Security wants a data-scientist persona that can train and a second persona that can only deploy, without hand-writing every action from scratch. What should they use?
- A model-artifact bucket looks private in the ACL, but its bucket policy still allows Principal "" on a report prefix and allows non-TLS gets. IAM roles already look tight. What should they apply?
- Twelve operators each have a unique inline policy copy. When endpoint-update must be removed from analysts, half the copies are missed. What should they use?
- Training jobs and the production endpoint still start with default-VPC public IPs. Staff need to isolate the ML system, not attach an already-designed VPC. What should they design?
- Notebooks in a private subnet must call SageMaker APIs without a NAT path to the public SageMaker endpoint. What should they create?
- Studio and Model Monitor jobs run in a custom VPC. Schedules fail because the job cannot reach Amazon S3 or CloudWatch without a public hop. What should they add?
- A water-plant endpoint’s security group allows 0.0.0.0/0 on the inference port “because the VPC is private.” A compromised host in another subnet can invoke it. What should they restrict?
- The data lake is already encrypted. The production endpoint’s EBS volumes and the Model Monitor output prefix still use the AWS-managed default, which agency policy forbids for live scoring data. What should they apply?
- A CodeBuild stage and a BYOC container still embed a database password for a permit-desk feature lookup. Where should that credential live?
- A regulator asks who changed the production endpoint last quarter and whether encryption settings drifted. CloudWatch metric dashboards cannot answer that. Which pair should they use?
- The CodePipeline and CodeBuild role can iam:PassRole to any role and sagemaker: in every account. What should they do?
- A buildspec exports an IAM user’s access key to push to Amazon ECR and call SageMaker. The key lives in the CodeBuild project environment. What should replace it?
- A custom training image can still reach the public internet and an unexpected webhook. The job must not initiate outbound calls. What should they enable?
- An inference pipeline runs two containers on the same host. Agency policy requires encryption in transit between those containers, not only TLS from the client to the endpoint. What should they enable?
- An application role receives AccessDenied on sagemaker:InvokeEndpoint (VPC and KMS look fine). What should they debug?
- Data capture now writes raw request bodies. A developer later finds national IDs in the production capture prefix. What should they use to find that PII in live artifacts?
- An auditor asks for the immutable record of what a fire-inspection model is for, its risk rating, and its last evaluation. The Model Registry has versions but no intended-use document. What should they create?
- A records request asks which exact training set and job produced last March’s production model. Registry version notes are incomplete. What should they use?
- Experiment notebooks and the production endpoint share one account and one IAM neighborhood. Sandbox principals can still reach live artifacts. What should they use to isolate production ML?
- Last year’s review required KMS and VPC isolation. A new endpoint launched this month with neither, and nobody noticed until the audit. What should detect that drop continuously?
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