BDPC practice questions
CertiProf · BDPC · 300 questions
Original practice questions for the CertiProf Big Data Professional Certificate (BDPC™), covering the 4 V's of Big Data, Hadoop/HDFS, MapReduce, NoSQL, Spark, Kafka-style ingestion, cloud computing, and web analytics.
This course contains the use of artificial intelligence.
About the BDPC exam
The vendor sells this as a course-and-exam bundle. The price shown is for the bundle, not a standalone exam voucher.
- Course + exam
- $200 USD
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- English, Spanish, Portuguese, French
- Format
- Online exam, one attempt included with purchase; digital badge issued by Credly
Exam details published by the vendor, checked 25 August 2026. Vendors change fees and formats without notice — confirm on the vendor's own page before you book.
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Big Data integrity / fundamentals · 30 questions
- A county assessor's office still runs property rolls in a single relational database. Staff ask when their workload becomes Big Data instead of ordinary reporting. Which condition best marks that shift?
- A city manager wants one council-ready sentence defining Big Data beyond "lots of data." Which definition fits CertiProf BDPC fundamentals?
- A municipal utilities board asks what business benefit they should expect if they invest in Big Data analytics for outage patterns. Which benefit is the soundest expectation?
- A library consortium compares nightly batch SQL reports on catalog tables with a new workload that analyzes clickstreams and sensor feeds together. How do those workloads differ in Big Data terms?
- A parks department debates whether Big Data applies only to an enterprise-wide Hadoop program or also to a departmental analytics effort on semi-structured reservation and IoT feeds. Which view matches BDPC scope thinking?
- A public-health analyst worries that dashboards look precise while underlying citizen surveys are incomplete. Which integrity principle should guide the team?
- A 311 center hears about cognitive systems trained on huge corpora of documents and interactions and wonders what Big Data problem class that pattern illustrates. Which characterization is most accurate for BDPC fundamentals?
- A regional transit agency asks which operational domains sit inside Big Data's practical reach. Which answer best reflects BDPC breadth?
- A wastewater SCADA historian grows by terabytes of sensor readings each month, and the warehouse team hits storage and query walls on the conventional platform. Which of the 4 V's is primarily illustrated?
- A traffic-management center needs curb-lane occupancy updates every few seconds rather than overnight batches. Which of the 4 V's does this requirement primarily stress?
- An emergency-management office mixes CAD logs, radio transcripts, map layers, and smartphone photos after a storm. Which of the 4 V's does this mix primarily illustrate?
- A housing authority distrusts scraped rental listings that conflict with permit records. Which of the 4 V's is the central concern?
- A smart-city pilot claims "we have Big Data" because cameras exist. Staff must map which of the 4 V's actually stress their stack. Which approach is most aligned with BDPC fundamentals?
- A clerk assumes any JSON API feed is "volume" when the real pain is dozens of drifting schemas across partners. Which correction is accurate?
- A flood-alert team wants instant social posts on the operations dashboard, but analysts flag rumor risk. Which 4 V tension should leaders recognize?
- A mayor's innovation office lists predictive maintenance, citizen sentiment analysis, and dynamic routing as candidate pilots. What Big Data rationale best justifies treating them as application classes?
- A CIO asks what "managing Big Data" includes beyond buying disks. Which answer matches BDPC fundamentals?
- A mid-size city sketches an ecosystem diagram and places a serving dashboard in the ingestion layer while putting Kafka-style collection in the serving layer. Which correction reflects conceptual ecosystem roles?
- A budget office wants analytics that find cost drivers across multi-year procurement files plus IoT meter data. What is the primary purpose of that Big Data analysis?
- Transit supervisors want a wallboard of live vehicle and delay indicators rather than weekly PDF summaries. What Big Data serving pattern does this request represent?
- IT lists failures including schema drift, cluster cost overruns, skill gaps, and batch windows too slow for same-day decisions. What do these illustrate in BDPC terms?
- After naming challenges, the team maps remedies: distributed storage for scale limits, parallel processing for slow jobs, NoSQL for flexible structures, streaming for timely signals, and cloud elasticity for spiky demand. Which statement best captures the BDPC skill being practiced?
- Finance prefers vertically scaling one large SQL server; engineering argues for horizontal Big Data patterns for clickstream and sensor mashups. Which comparison is most accurate?
- Public works assumes vendors "guarantee" sensor truth, yet analysts still validate anomalies before publishing street-condition scores. Which veracity operations principle applies?
- A three-person grants office maintains a tidy 50,000-row spreadsheet that conventional tools handle comfortably and asks if they need a Hadoop cluster. What is the best BDPC-aligned answer?
- A city council plans zoning changes and wants mobility traces, 311 complaints, and census tables mashed together before voting. What Big Data benefit is the council primarily seeking?
- A water-plant modernization pitch cites Industry 4.0 and continuous sensor floods from pumps and valves. How should staff correctly tie that pitch to Big Data fundamentals?
- A vendor demo sells one appliance as ‘the entire Big Data ecosystem.’ What should municipal architects emphasize instead?
- Solid-waste managers believe truck miles would drop if stop-level service data and live traffic were analyzed together. Which Big Data benefit does that goal illustrate?
- Emergency managers face conflicting reports across radio, apps, and sensors and want faster situational awareness. What Big Data stance best fits?
Sources and applications · 30 questions
- A 311 call center stores citizen-to-agent conversations and free-text request notes for later analysis. Which source class does that primarily represent?
- Residents use a transit mobile app that writes every trip-plan request to backend logs. Which Big Data source class is that interaction?
- Streetlight controllers report status every minute with no operator in the loop. What source class is that telemetry?
- A data-inventory workshop drops SMS complaints, transit-app clicks, and SCADA heartbeats into one unlabeled ‘sensor’ bucket. What correction should facilitators make?
- Before buying tools, a city maps which source classes feed ingest, lake storage, processing, and serving layers. Why is that mapping valuable?
- Public health wants search- and symptom-proxy signals to anticipate clinic load. Which application-pattern idea does that reflect?
- Parks marketing scrapes and surveys citizen comments about a waterfront redesign. Which application pattern is that closest to?
- Police analysts discuss predictive deployment models fed by historical incidents and sensors. What BDPC-aware framing is appropriate?
- A municipal fleet pilot proposes usage-based insurance-style premiums from vehicle telematics. Which application pattern does that illustrate?
- A regional open-data site publishes CSVs and APIs that other agencies mash up. How should BDPC candidates classify that feed?
- After a festival, operations combines crowd social posts with gate-entry turnstile counts. What does that mashup demonstrate?
- Bridge strain gauges stream measurements into a central store without human keystrokes per reading. Which classification fits best?
- Housing inspectors want maps of complaint clusters overlapping inspection outcomes. Which multi-source civic application idea is that?
- The city portal analytics team treats page events as inputs for service design. Which source framing is correct?
- Nightly ETL assumes one fixed schema, but a new chatbot begins dumping unstructured transcripts. What Big Data lesson applies?
- Wastewater wants early detection of pump anomalies from sensor streams plus work-order history. Which application class fits?
- Snow-plow dispatch blends storm forecasts, road sensors, and prior route times. What application pattern is that primarily?
- A library wants reminder notifications tailored from checkout patterns without creepy overreach. Which application stance fits?
- Emergency dashboards need second-level machine telemetry, while monthly grant statistics do not. What principle should architects apply?
- Digitized historical permits sit in archives, while the live permitting system emits events as filings occur. How should product owners treat those sources?
- Fire, EMS, and hospital partners want one shared incident timeline, but each still exports overnight CSV drops into separate folders. What Big Data source challenge should the city tackle first?
- A city’s online feedback channel is flooded with bot-generated comments that praise or attack every park project. Before leaders trust a sentiment dashboard, what source issue must analysts treat?
- Every new smart water meter adds device tags, interval series, and location attributes until the utilities “source catalog” is unmanageable. What source-management problem is the city facing?
- A town already has traffic cameras and transit fare cards, and leaders ask for “AI magic” that predicts household income for every address. What application choice best respects available sources?
- Building-permit comments arrive as free-text narratives from inspectors, while inspection scores arrive as numeric fields. Which Big Data characteristic does the narrative most clearly drive?
- A rideshare data-sharing agreement will push external trip aggregates to the city every night through a partner interface. How should the analytics team classify this feed?
- Counseling hotline notes are person-to-person records with highly sensitive personal details. What should constrain the analytics scope for this source?
- Mis-calibrated air-quality sensors keep reporting false spikes that then drive public health alerts. What lesson about sources and applications should the city apply?
- Bus AVL pings, bike-share dock occupancy, and parking sensors will feed one civic mobility application. What source pattern best describes this design?
- Council asks how machine-to-machine water meters turn into dollar savings. What narrative should staff present?
Big Data architectures · 30 questions
- A city enterprise architect must explain ingestion, storage, processing, and serving layers to non-technical directors. What is the main purpose of those layers?
- Leaders want interactive queries over huge municipal log stores, similar to web-scale query systems. Which architecture pattern should the team emphasize?
- Finance accepts T+1 batch closes, while traffic operations needs minute-level metric refresh. How should architecture reflect both needs?
- A research hospital–city partnership will ingest clinical free text plus structured operations data to support assistive analytics. Which architecture pattern does this sketch reflect?
- A civic video platform for council meetings must encode, store, recommend, and serve recordings at scale. Which reference architecture lesson applies?
- IT runs analytics virtual machines on a virtualized farm and must place Big Data services correctly. What architecture guidance fits?
- Emergency management wants observation ingest, model runs, and public alerts as layered services. Which architecture pattern matches?
- A festival ticketing spike must capture purchase events and analytics without collapsing checkout. What architecture emphasis is required?
- Workforce development wants to map skill connections across regional employers as relationship-heavy data. Which architecture pattern class fits?
- A municipal payments platform needs streaming features that flag anomalous transactions in near real time. Which architecture pattern applies?
- Where do HDFS, YARN, and processing engines typically sit among city Big Data architecture layers?
- Agencies dump every CSV into object storage with no processing or serving plan. What architectural mistake is this?
- The same civic program keeps history in HDFS, user profiles in NoSQL, and finance marts in a warehouse. What architecture idea does this illustrate?
- Architects describe a nightly batch correction path alongside a speed layer that feeds live operational metrics. What architectural idea are they applying?
- Shipping all raw camera video to a distant site before any filter exhausts the city’s WAN budget. What architecture principle should guide the redesign?
- A city lake holds the same mobility and permitting facts, but council packets need polished PDF summaries while dispatch consoles need sub-second lookups. Which architectural idea best explains serving both consumers from one platform?
- Health inspection records and building permits will share a municipal analytics platform, yet privacy rules differ sharply. What should the architecture treat as a cross-cutting design concern rather than a late bolt-on?
- A metro plans a mashup of 311 tickets and continuous IoT curb sensors. Which set of reference-architecture traits should drive the design rather than a favorite vendor slide?
- During a storm, 311 and sensor feeds surge. Which responsibilities belong primarily to the ingestion layer of the Big Data architecture?
- Each night the city enriches parcel records with mobility indices before morning planning views refresh. Where should that enrichment job live in a layered Big Data architecture?
- Bridge-strain gauges must raise alerts within minutes when thresholds trip, yet the municipal warehouse only lands overnight. What architectural response fits?
- Analysts across departments cannot tell which curb-sensor feed is authoritative for downtown counts. Which architectural capability is missing?
- A county hosts analytics for several townships on one shared platform. What multi-tenant concerns must the architecture address?
- Traffic cameras generate continuous video that would overwhelm core ingest if every frame were shipped unchanged. Which architecture option best addresses volume and velocity?
- Emergency operations depend on HDFS-backed analytics and serving clusters during disasters. What should architects align when designing resilience?
- Permitting remains authoritative in a departmental SQL system while a Big Data platform will power citywide analytics. How should the architecture integrate them?
- Open-data crunch weeks spike compute once a month, then demand drops. Which cost-aware architecture choice fits better than always-on oversized clusters?
- Directors ask whether yesterday’s mobility enrichment job finished successfully before morning briefings. What must the architecture include?
- Analysts run multi-hour scans directly against the live permitting OLTP database and clerks report timeouts. What architectural correction addresses the anti-pattern?
- The architecture board maps ingestion, processing, storage, and serving layers before choosing Hadoop, MapReduce, NoSQL, Spark, or Kafka components. Why is that sequencing sound?
Distributed computing with Hadoop · 30 questions
- Citywide smart-meter history no longer fits or processes in time on one powerful server. What is the core rationale for moving to cluster computing?
- Council staff want a plain-language briefing line that defines Hadoop without vendor hype. Which statement is most accurate at concept level?
- IT staff use “Hadoop” and “MapReduce” as if the words were identical. How should a BDPC-oriented explanation separate them?
- Meter archive files must be processed in parallel across the cluster. What foundational distributed-computing practice enables that parallelism?
- Architects brief operations on Hadoop-style cluster roles. Which description matches the classic coordinator versus worker pattern?
- Why would the city store multi-terabyte camera index archives on HDFS rather than relying on a conventional NAS share alone?
- An analyst job reads a large HDFS dataset. Conceptually, how does the read path involve cluster roles?
- Ingest pipelines write large files into HDFS for durable analytics storage. What conceptual write behavior should operators expect?
- Which HDFS characteristic best matches typical Big Data analytics workloads for municipal archives?
- A DataNode fails overnight, yet morning mobility jobs still find their input blocks. Which HDFS characteristic primarily explains that resilience?
- A city GIS archive of multi-terabyte aerial imagery must land on the municipal Hadoop cluster. How does HDFS typically store each huge file?
- Public-works ops asks which Hadoop role holds the namespace metadata versus the actual block bytes for storm-drain sensor archives. Which distinction is correct?
- Planning, transit, and public-health teams all want CPU and memory on the same city Hadoop cluster. What is YARN’s primary role?
- A county architect insists storage and resource management are separate Hadoop layers. Which statement correctly separates HDFS from YARN?
- Each month the city mashes multi-agency open-data dumps for a retrospective equity report. Where should that heavy batch mashup run?
- Next year’s IoT expansion will roughly double traffic-sensor volume on the municipal lake. How does a Hadoop cluster typically grow capacity?
- Transit analysts want MapReduce tasks to run close to the AVL block replicas they read. What benefit does data locality primarily provide?
- A parks department has a 2 GB CSV of playground inspection notes used by three analysts. What is the sound capacity judgment?
- Council marketing claims HDFS replication means the city never needs operational practices for disk or node loss. What is the accurate view?
- A lab standup checklist says the city will configure HDFS on the new cluster. At BDPC depth, what should staff understand?
- Planning’s equity model and public-works’ pavement job both submit heavy work to the shared city cluster. What multi-tenant concern should capacity planning address?
- Years of bus AVL archives are rarely queried but must stay available for deep historical studies. What role fits HDFS well?
- Daily 311 partitions land once as immutable files and many overnight jobs read them. Which HDFS-aligned pattern is that?
- Environmental sensors drop millions of tiny one-reading files onto HDFS each day. What classic problem should the platform team anticipate?
- When placing HDFS replicas for water-quality archives, why do replication policies care about racks or failure domains?
- Semi-structured JSON 311 payloads and free-text inspector logs need a landing zone before schema-on-read jobs. How does HDFS support that Variety need?
- A vendor claims a Hadoop appliance removes any need for architecture thinking about storage, resources, and workloads. How should the city respond?
- Jobs that ignore locality haul raw blocks across the data-center fabric during large shuffles. What constraint becomes visible?
- Analysts across departments can read shared open-data paths, but not everyone should delete sensitive HDFS directories. What awareness is required?
- After HDFS and YARN are in place, the analytics team wants parallel batch processing over those stored blocks. How do the layers relate?
Parallel processing with MapReduce · 30 questions
- The city wants a parallel count of citation reason codes across a huge distributed ticket archive. How does MapReduce organize that work?
- During a citywide MapReduce job, something must assign map and reduce tasks to workers on the cluster. Which role description fits?
- Staff training asks for the correct order of MapReduce stages for a batch 311 analytics job. Which sequence is correct?
- An instructor compares MapReduce to filtering lines with pipeline commands and then aggregating counts. What teaching point is that analogy meant to convey?
- Analysts must count how often each service category appears across millions of 311 text blurbs. Which MapReduce thinking applies?
- A city 311 desk runs a MapReduce job over complaint tickets so analysts can tally each reason code. What should each mapper emit during the map phase?
- After mappers emit (reason-code, 1) pairs for municipal 311 tickets, what must the shuffle and group stages guarantee before reduce runs?
- In a MapReduce tally of transit delay reason codes, what is the reducer's primary responsibility?
- A metropolitan planning office needs last year's mobility indices recomputed overnight from archived fare-tap and sensor files. Why is classic MapReduce a strong fit?
- A municipal payment portal must raise a fraud flag within a fraction of a second on each card authorization. Why is overnight MapReduce a poor primary design?
- While counting commutative 311 reason codes, engineers want fewer bytes crossing the network during shuffle. Which optional MapReduce idea helps?
- A county open-data lake stores permit archives as many HDFS blocks. What generally increases how many map tasks can run in parallel for a MapReduce job?
- During a citywide log-parse MapReduce job, one mapper node crashes mid-task. What resilience behavior should operators expect in a classic MapReduce framework?
- Housing analysts who already write SQL want ad-hoc summaries over Hadoop tables, while an ETL team prefers scripted dataflow transforms. How should the city choose between Hive and Pig?
- A housing open-data team wants ad-hoc, SQL-like queries over tables stored on HDFS without hand-writing low-level MapReduce Java for every ask. Which layer best matches that need?
- Public-works engineers need scalable ETL-style transforms to clean messy permit text fields before loading a warehouse. Which Hadoop-era tool is aimed at that scripting/dataflow style?
- A parks department proposes putting live festival ticket checkout—inventory locks, payments, and immediate confirmations—on Hive tables in HDFS. What is the sound judgment?
- A city needs to re-encode millions of archived sensor files into a new columnar layout without aggregating across keys. Which MapReduce pattern awareness is most relevant?
- In a MapReduce job counting 311 reason codes, one mega-popular code sends almost all intermediate values to a single reducer and the job crawls. What problem does this illustrate?
- A few map tasks on a slow municipal worker drag out an otherwise healthy MapReduce job. Which classic framework idea races duplicates to mitigate stragglers?
- Data stewards want to confirm how many permit records entered and left each stage of a nightly MapReduce cleanse. What MapReduce observability feature supports that check?
- Building a mobility data mart requires cleanse, then join, then aggregate stages that do not fit cleanly in one MapReduce job. What pipeline approach is appropriate?
- Transit analysts need to join massive fare-tap logs to daily weather observations by date using Hadoop-era batch processing. Which statement best frames the MapReduce role?
- When designing a MapReduce job that totals citations by violation type, what primarily determines which values a reducer sees together?
- Shuffle traffic between mappers and reducers is saturating municipal cluster links during a large MapReduce job. Which performance lever directly targets intermediate map output size on the wire?
- Each night a city web team must parse raw access logs into structured session features for analysts. Why is batch MapReduce a natural fit?
- In a modern Hadoop deployment used by a regional transit agency, how does MapReduce relate to YARN?
- Analysts only need a straightforward group-by on already-cataloged Hive tables of recycling-tonnage facts. What is the better abstraction choice?
- After a MapReduce mobility job finishes successfully, where do durable final results typically land for downstream civic services?
- A municipal data platform already runs nightly MapReduce batch jobs on HDFS and plans NoSQL serving plus Spark streaming next. How should leaders view MapReduce's place?
NoSQL databases · 30 questions
- A city's citizen-profile service keeps adding optional attributes for new programs, and rigid relational migrations delay each release. Which approach best fits this evolving, high-scale profile data?
- A municipal finance office must post multi-row ledger entries that either all commit or all roll back across related accounts. Which store is the most appropriate primary system?
- A multi-region 311 status store must keep serving during network splits, yet operators also want strong consistency everywhere. What does the CAP theorem imply for this design?
- During a WAN blip between regions, a public city status page should keep answering rather than go dark, even if some counts are briefly stale. Which CAP-oriented choice fits best?
- Ballot tabulation mirrors across election sites must never display conflicting totals to the public. Under a network partition, which consistency stance is most appropriate?
- Which set of traits best characterizes many NoSQL systems used for municipal Big Data workloads?
- A city mobile app needs to look up session tokens quickly using only a session id. Which NoSQL type fits this access pattern best?
- Permit applications arrive as nested JSON-like records with sections that differ by permit type. Which NoSQL type is the best fit for storing these semi-structured civic records?
- Utility meter readings stream in as write-heavy time-series appends that grow sparse columns over long periods. Which NoSQL family is typically suited to this pattern?
- An integrity office wants to explore how vendors, contracts, and employees connect when hunting fraud rings. Which NoSQL type best supports relationship-centric queries?
- One digital-city program stores citizen profiles as documents, fraud links in a graph, and hot session data in a key-value cache. What persistence approach does this illustrate?
- Which description best matches Apache Cassandra as commonly presented in Big Data NoSQL syllabi?
- After a citizen updates a profile, two readers briefly see different versions before the system converges. What NoSQL behavior does this illustrate?
- A smart-city IoT desk lands varied sensor payloads whose shapes keep changing and cannot wait for one universal upfront schema. Which approach aligns best?
- Analysts still run SQL-like Hive queries over lake data that also feeds a NoSQL serving tier for the citizen portal. What role does Hive play in this syllabus-style framing?
- Data engineers use Pig scripts to cleanse and reshape municipal feeds before loading them into NoSQL stores. How is Pig best characterized in this framing?
- Millions of parking-meter heartbeats arrive each hour and overwhelm a single primary database writer. Which NoSQL-oriented strategy best addresses this write pressure?
- Developers assume they can filter a civic NoSQL store on any field as freely as a normalized SQL database. What caution should the architect emphasize?
- The citizen portal is served from NoSQL, but planners need heavy multi-year group-by analytics across all districts. Where should those analytical aggregations typically run?
- Operators place a Redis-like key-value cache in front of citizen profiles to speed reads. What must they remember about that cache tier?
- Engineers are modeling citizen timeline documents for a NoSQL store. Which modeling principle should guide the design?
- A regional NoSQL cluster for emergency services must survive the loss of one datacenter site. What architecture theme does this requirement highlight?
- Transient traffic incidents should disappear from a hot operational store after a set period without manual cleanup jobs. Which NoSQL operational feature fits this need?
- A budget office needs complex multi-table joins across funds, vendors, and appropriations for mandatory financial reports. Why is a typical NoSQL document or KV store often a poor primary fit?
- Cluster operators want stricter consistency for ballot-status reads but looser consistency for anonymous traffic-map pings. What NoSQL concept enables that per-request-class choice?
- A parks department must store photo metadata, free-form tags, and view counters for each trail report in one flexible record instead of rigid columns. Which approach best fits this variety?
- A city wants high-scale citizen profile lookups in NoSQL but must keep tax billing in a relational system with strong ACID controls. What migration pattern fits?
- Several towns share one managed NoSQL service for 311 case documents. How should the platform isolate each town’s data at a basic tenancy level?
- During a festival, almost every write for a street-closure feed uses one popular event id as the partition key and one shard saturates. What problem is this?
- A transit desk uses Spark and Kafka to refresh arrival estimates every few seconds, then riders query those estimates through a fast document store. What role is NoSQL playing?
Stream processing with Spark · 30 questions
- A traffic bureau wants to act on sensor events as they arrive instead of waiting for tonight’s batch files. What does stream computing emphasize?
- The same automatic vehicle location feed can produce a nightly MapReduce ridership summary or a continuous delay detector. What contrast should planners remember?
- Bridge strain gauges must page on-call engineers within seconds when readings cross a safety threshold. Which streaming application class is this?
- A municipal operations center wallboard must refresh curb-occupancy and incident counts throughout the day from live feeds. What streaming use does this illustrate?
- Street-sensor analytics must run with limited memory while seeing only one pass or a sliding window of events. What trait should streaming algorithms accept?
- A license-plate camera desk wants a fast test of whether a plate was already seen today without storing every plate string in a huge exact set. Which structure fits?
- Operators ask what a standard Bloom filter can get wrong when testing whether a plate is in today’s set. Which statement is accurate?
- A city selects Apache Spark as the engine for micro-batch and stream analytics on curb-sensor events. What does that choice primarily recognize?
- An iterative model that revisits the same civic graph many times runs far slower on classic disk-heavy MapReduce than on Spark. Why?
- A municipal architect places Spark beside HDFS, YARN, Kafka, and related tools rather than treating it as a lone product. What idea does that convey?
- On a Spark cluster analyzing bus GPS partitions, which mental model of roles is correct?
- Training material describes Resilient Distributed Datasets for a flood-sensor analytics job. What core idea should staff take away?
- Developers chain map and filter calls on sensor RDDs, then call a count that finally returns a number. What distinction matters?
- A Spark job on neighborhood sensor partitions runs as pipelined work across the cluster. How is that execution commonly described at a high level?
- A syllabus example uses Spark for PageRank-style scoring of linked civic web pages. What capability does that highlight without requiring code memorization?
- Analysts must count 311-style social alerts in sliding five-minute windows as messages arrive. Which streaming analytics pattern is this?
- Duplicate curb-sensor events could inflate occupancy counts if the streaming job treats every delivery as unique. What concern does that raise?
- A streaming app tracks each bus’s last-seen timestamp so stale vehicles can be flagged. What concept does maintaining that per-bus value illustrate?
- A team describes Spark Streaming–style processing as short batches fired back-to-back to approximate continuous work. What mental model is that?
- A public-works ETL that once ran as classic MapReduce is being rewritten as Spark batch jobs for speed and richer APIs. What shift does this represent?
- A transit analytics team caches several huge AVL history tables in Spark memory just in case, and executors begin failing with out-of-memory errors. What lesson should the program lead take?
- A water utility wants Spark to enrich live meter readings with last year's HDFS partition history while new deltas keep arriving. How should that architecture be described?
- A finance desk wants unusual permit-fee refunds flagged as payment events stream in, not only after a nightly batch. Which approach fits that need?
- Public works needs continuous plow GPS points to refresh street coverage maps during a storm. What processing pattern matches that need?
- Flood sensors publish faster than a city's stream processors can keep up. What operational concept should architects plan for?
- After a node failure, a parking-citation streaming job must resume without losing its rolling window state. What mechanism supports that recovery?
- A clerk needs a nightly 10-row lookup of holiday parking exceptions. What is the most appropriate tooling judgment?
- A web crawler for open-data portals fetches many duplicate URLs before expensive parsing. What lightweight structure can help dedupe early in the ingest path?
- Analysts want familiar SQL to join large municipal DataFrames in Spark without writing low-level RDD code. What access style does that describe?
- A city operations center streams 311 and AVL events through messaging topics that Spark jobs continuously consume. What typical pairing does this illustrate?
Data ingestion · 30 questions
- Department systems generate continuous events that must land reliably in the analytical platform. What is the primary role of a data ingestion system?
- Traffic sensors should publish once while fraud, maintenance, and dashboards each consume independently. What role do messaging systems play?
- A city event bus keeps ordered records in distributed logs that apps publish to and subscribe from by topic. Which technology pattern matches that design?
- Fare gates emit tap events onto the city's event bus as riders enter. In Kafka terms, what role are the gates playing?
- A Spark job and a separate audit service both read the same fare-tap topic without blocking each other. What Kafka role do those readers play?
- Servers in the city Kafka cluster store topic partitions and serve produce and fetch requests. What are those servers called?
- Operations wants AVL locations, fare taps, and 311 requests kept as separate streams on the event bus. How should those streams be organized?
- A high-volume 311 topic must scale across workers while preserving order for each citizen request key. What Kafka mechanism provides that balance?
- Emergency alerts should be published once while many departmental systems process them asynchronously. Which topic mechanism does that describe?
- After a bad deploy, analysts need to re-read last Tuesday's sensor topic. What Kafka attribute makes that possible within the configured window?
- Stadium concert weekends drive a surge of transit and safety events. How do Kafka-style systems typically scale to absorb that growth?
- A smart-waste pilot will generate millions of small IoT messages each hour. Which Kafka attribute is most directly relevant?
- Real-time alerting workers should share the 311 topic load, while a separate audit pipeline must see every message on its own. How do consumer groups enable that?
- A parser bug corrupted last week's derived parking metrics. After fixing the code, what Kafka operational benefit lets the team rebuild cleanly?
- Architects propose keeping seven years of raw AVL solely inside Kafka with multi-day retention settings unchanged. What correction should leadership make?
- A city 311 bus starts shipping optional JSON fields for newly added service categories while older mobile apps still consume the topic. What ingestion design best tolerates that schema evolution?
- A parking-sensor topic uses at-least-once delivery, so a garage analytics consumer may see the same occupancy event more than once. What consumer practice matches that delivery model?
- Transit events for an entire metro region are spread across many Kafka partitions for throughput. What ordering guarantee should planners expect?
- Flood-alert producers keep publishing while a slow emergency dashboard consumer falls behind. What ingestion design principle should the city apply?
- A municipal payments topic carries fee and refund events that many microservices can see on the shared bus. What security basic should the platform enforce?
- Operations wants traffic-signal telemetry available if the primary datacenter fails. Which ingestion continuity approach fits that goal?
- Streetlight sensors speak constrained field protocols, while the analytics platform expects Kafka producers. Which pattern best bridges that gap?
- Permit-office staff update a transactional database all day, and analysts need those changes as near-real-time events on Kafka. Which ingestion pattern fits?
- A malformed sanitation-route event repeatedly crashes a parser in the main Kafka consumer group. What handling approach keeps the healthy stream moving?
- Live traffic analytics start showing stale congestion maps even though producers are publishing. Which operational metric should on-call engineers watch first on the bus?
- Dispatchers need second-level visibility into ambulance-bay status, but the current feed is a nightly FTP of CSV dumps. Why is that pattern a poor fit?
- One city Kafka topic must refresh a low-latency NoSQL hot store and also land long-term copies in HDFS. Which architecture idea applies?
- A grants team asks for exactly-once end-to-end processing of every subsidy event across producers, Kafka, and multiple sinks. What tradeoff should architects communicate?
- Agencies keep inventing incompatible topic names for similar water-quality events, and producers cannot find the right bus. What governance practice helps?
- A county moves from self-hosted Kafka to a managed cloud messaging service for 311 events. What stays conceptually true?
Cloud computing · 30 questions
- A planning department wants elastic analytics capacity without buying every server for the basement rack. How should they define cloud computing for Big Data in that context?
- Regional analysts need to reach Big Data clusters without traveling to a single locked computer room. Which cloud access characteristic matters most?
- IT wants Hadoop- or Spark-style stacks without tying every node to a named physical rack forever. What cloud underpinning enables that flexibility?
- Election-night traffic analytics need a large burst of capacity, then quiet days afterward. Which cloud benefit should the city leverage?
- Open-data crunch seasons are short, but the city previously bought large always-on clusters. Which cloud cost-flexibility idea applies?
- A grant-funded pilot needs a short-lived Spark-capable environment within hours, not months of hardware procurement. Which cloud benefit is illustrated?
- A regional consortium runs shared analytics on capacity owned by a cloud provider, under contracts and controls the cities accept. Which ownership model is that?
- Police evidence analytics must remain on infrastructure dedicated to the agency because of policy. Which ownership model fits that constraint?
- Sensitive resident datasets must stay on-premises, but the city wants to burst heavy model training into public cloud capacity. Which model describes that design?
- The city installs and manages its own Hadoop stack on virtual machines rented from a cloud provider. Which service-range model is that?
- A city analytics team wants Spark and Kafka-like capacity without patching brokers or cluster nodes every week. Which cloud service range best matches that goal?
- After a county lake prepares daily civic metrics, leadership wants a hosted dashboard product citizens never see under the hood. Which cloud service range fits analytics consumption?
- A metro moves its Big Data lake to a public cloud. The provider hardens the foundation, yet auditors still ask who classifies resident records and manages staff identities. What shared-responsibility idea applies?
- Policy requires resident analytics datasets to remain inside the national region. How should that rule shape cloud selection for the municipal lake?
- Traffic cameras dump large video objects into one cloud region, then analysts repeatedly pull the same files into another region for nightly jobs. Which cost and architecture concern should the team address first?
- A town redesigns its Hadoop-era lake for the cloud and needs a durable landing zone for raw civic files. Which pattern commonly replaces or fronts classic HDFS in that setting?
- A parks department runs heavy MapReduce-style jobs only a few nights each month. Which cloud pattern best matches spinning capacity up for the job and releasing it afterward?
- Leadership wants two public clouds “for safety” for the same Kafka and Spark stack, but the city has deep skills in only one provider. What caution should the Big Data lead raise?
- A county lake holds critical analytical datasets that must survive a regional outage. Which cloud practice best supports disaster recovery for those stores?
- During a cloud lake review, auditors find a storage bucket of citizen personally identifiable information left publicly readable. What security baseline judgment is correct?
- Permit-portal click events spike during storm season and streaming consumers fall behind. How does cloud elasticity primarily support the Velocity V in this scenario?
- A transit agency must retain years of vehicle telemetry for trend studies. Which cloud capability most directly supports the Volume V?
- A city builds every lake job against proprietary managed APIs that have no portable equivalent. What lock-in awareness should architects apply?
- An air-gapped utilities operations network needs OT analytics that cannot reach the public internet. What cloud-chapter conclusion is appropriate?
- Data scientists want to try new Spark job ideas without risking the production civic lake. Which cloud practice best fits that need?
- Staff across departments need access to cloud analytics workspaces without a separate password vault for every tool. What foundation should the city prioritize?
- Parks and transit both run Spark jobs on the same cloud account, and finance wants each department to see its share. Which practice supports that visibility?
- A public-health adjacent dataset will land in the city’s cloud lake. Beyond ordinary open-data rules, what design factor should influence the cloud Big Data layout?
- A city must move its on-prem Kafka and Spark stack to the cloud. What contrast captures the main migration-style choice?
- After moving HDFS, Spark, and Kafka-style components into a public cloud, a trainee asks whether BDPC concepts still apply. What is the best response?
Web analyzer application · 30 questions
- A city wants a web-analyzer application that shows how residents use the municipal portal. Which end-to-end purpose best describes that architecture?
- Which primary input typically feeds a municipal portal web-analyzer application?
- Engineers designing collection for a civic web analyzer debate how to capture portal behavior. Which technology options fit that collection layer?
- Raw portal events arrive faster than daily aggregates can be finalized. Where should those raw events typically land first in a web-analyzer architecture?
- The civic web analyzer needs both accurate daily visitor sessions and near-real-time live counters. Which processing approach aligns batch and stream for that goal?
- A city UX team needs top pages, conversion funnels, and portal error rates without querying the raw clickstream lake each morning. Which serving choice best fits a municipal web analyzer?
- Before a municipal portal analyzer can compare renewals across departments, what application-code concern must be settled first?
- A library portal records many page hits from the same anonymous visitor. How should the web analyzer turn those hits into usable visit units?
- A parks department dashboard shows huge overnight 'traffic' that never leads to permit completions. What web-analyzer practice keeps civic UX metrics meaningful?
- Permit staff notice many residents start an online application but never finish. Which web-analyzer outcome best informs a redesign?
- A city legal notice limits unnecessary personal data in portal telemetry. What privacy practice should the web analyzer follow?
- Portal ops need immediate notice of error spikes, while content editors only need yesterday's popular pages. How should latency be matched inside the analyzer architecture?
- A civic portal must feed both Spark jobs and a hot counter store from the same page events. Which ingestion pattern fits the web analyzer?
- Holiday week hits a city portal with huge traffic, many event types, and broken tags on some pages. How do the 4 V’s map to that web-analytics situation?
- A digital services office wants evidence before choosing between two portal layouts for license renewal. What should the analyzer support?
- Residents abandon 311 web forms when pages freeze or scripts crash. Beyond marketing popularity, what should the analyzer track?
- Parks, libraries, and the main city site all need analytics without mixing their data incorrectly. What architecture approach fits?
- A transit mobile app shows screens that behave like portal pages for analytics purposes. How should the city reuse its web-analyzer patterns?
- Marketing credits a 'renew license' campaign using last-click only. What analytical caution should the web analyzer’s consumers remember?
- Storage costs climb if every raw portal hit is kept forever at full fidelity. Which retention design fits a civic web analyzer?
- Analytics beacons on a city portal accept POSTed events from browsers. What security concern must designers address?
- Service desks wonder whether busy portal days predict long in-person counters. What cross-channel idea fits a privacy-aware web analyzer?
- Leadership celebrates the most-viewed portal page, yet few residents complete the related service. What metric lesson should the analyzer emphasize?
- Marketing keeps adding campaign tags to portal events without a rigid relational migration each time. Which lake-friendly approach supports that evolution?
- Executives want a weekly view of how residents adopt digital city services. What serving output should the web analyzer provide?
- Traffic on a licensing portal suddenly collapses or surges far beyond normal. What should anomaly alerting imply for responders?
- A city documents its portal analyzer as collection, ingest, store, process, and serve. What does that mapping illustrate?
- Editors need live page-view counters while analysts keep multi-year history. How should NoSQL and the lake share duties?
- A mis-tagged funnel step corrupted last month's permit metrics. Raw logs still exist. What resilience action should the analyzer support?
- A city redesigns its portal analytics stack and must pick collection, Kafka ingestion, Spark processing, a lake, NoSQL counters, and dashboards that work together. What selection principle should guide the capstone design?
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