Data Manipulation and Preparation

NCA-ADS · 46 questions

A city open-data portal must join millions of permit applications to parcel tables on a shared parcelid before exporting a CPU-friendly CSV. Which approach best fits GPU-accelerated tabular joining with NVIDIA RAPIDS?
A county public-health analytics team receives twelve monthly 311 extract files with the same columns and needs one GPU DataFrame for citywide KPI rollups. What should they do?
A transit authority ridership desk must attach contractor licenses to vehicle inspections and keep every inspection row even when a license is missing. Which join behavior should they use?
A water-utility sensor desk will merge several large GPU tables but first wants to cut memory pressure. What ordering best reduces GPU footprint before the multi-table merge?
An emergency-management fusion cell needs neighborhood-level mean incident wait times over a multi-million-row CAD extract on a GPU. Which approach best fits cuDF?
A housing-authority waitlist team finds that applicantid is int64 in one extract and string in another, so cuDF and pandas joins fail. What should they do first?
A courts clerk e-filing desk has a small zone-code lookup and a very large GPU fact table of filings. How should they enrich filings with zone labels efficiently?
A library attendance analytics job finds null values in a resident identifier that resembles an SSN under municipal privacy rules. What is the most appropriate next step?
A parks and recreation demand desk must clean parcel addresses before assessor valuation models. Which combination best improves quality?
A school-district enrollment analytics team has missing inspection scores. District policy forbids inventing scores when the inspection never occurred. What should they do?
A county assessor parcel desk must stop tickets where closedat is earlier than openedat. Which quality step best addresses that?
A 311 resident-service analytics team prepares an open-data release and must reduce privacy risk in free-text complaint notes. What should they do during cleaning?
A permitting and inspections bureau sees extreme water-meter spikes from known sensor glitches. Which outlier treatment best fits before modeling?
A fire/EMS response-time pipeline mixes legacy mainframe disposition codes with a modern ETL vocabulary. What quality step best reconciles them?
A public hospital district throughput desk must run nightly ETL over multi-gigabyte transit AVL log folders on municipal GPUs. Which approach best fits RAPIDS-scale ingest?
An elections operations (non-partisan) data team runs countywide property-tax extract transforms that already live in a multi-node Spark estate. When is Spark GPU acceleration a fitting choice?
A municipal fleet telematics desk wants a GPU-accelerated path from land-file CSV through cleaning into a feature store. Which sequencing best matches RAPIDS ETL?
A solid-waste route analytics team must speed Dask processing of EMS CAD extracts. What partitioning strategy helps GPU/Dask workers?
A county social-services case ETL spends hours on a pandas apply that uppercases and trims free-text notes row by row. What RAPIDS-oriented change should they make?
A city planning zoning desk needs an end-to-end GPU ETL that analysts can repeatedly query. What landing choice best fits a RAPIDS multi-stage pipeline?
A city open-data portal's nightly RAPIDS job fits on one GPU today, but next quarter the county will add several times more rows. How should they choose RAPIDS-only versus RAPIDS+Dask?
A county public-health team will train a housing affordability classifier and must encode zoning category codes. What feature-engineering choice best fits high-cardinality civic categories?
A transit ridership desk wants model features that reflect municipal response-time SLAs such as under 10 minutes, 10–20, and over 20. What should they engineer?
A water-utility leak-risk model should capture how rainfall and pipe age act together, not only separately. Which feature engineering step fits?
An emergency-management fusion cell wants to target-encode neighborhood labels for an incident model without leaking future outcomes into training. What practice should they follow?
A housing-authority waitlist analytics team sees water-meter and occupancy sensor columns with extreme outliers before cuML training. Which numerical preprocessing choice best stabilizes those civic sensor features?
A courts and clerk e-filing analytics desk wants permit-submission timestamps to drive demand models. Which feature engineering step best captures calendar patterns for those filings?
A library program attendance analytics team faces hundreds of rare contractor license categories that explode feature width under one-hot encoding. What categorical strategy best curbs that dimensionality growth?
A parks and recreation demand desk must train a bridge-failure risk classifier where confirmed failure labels are extremely rare. Which imbalance approach best addresses that severe minority class?
A school-district enrollment analytics team sees rare permit-fraud flags and wants to avoid naïvely duplicating minority rows in GPU memory. Which imbalance strategy is the better first choice?
A county assessor parcel desk wants more minority inspection-fail examples for training but must keep synthetic rows under governance review. What is the most appropriate approach?
A 311 resident-service analytics team finds on-time tickets overwhelm GPU training time for a triage model. Which imbalance tactic best reduces majority dominance when compute is the bottleneck?
A permitting and inspections bureau plans SMOTE-style synthesis for rare outbreak-related inspection flags. How should they avoid leaking future information into training?
A fire and EMS response-time analytics team applied an imbalance remedy to a fire-risk classifier. How should they judge whether the fix actually helped for rare risk cases?
A public hospital district throughput desk has dozens of highly correlated census and catchment features for a planner model. Which dimensionality reduction step best compresses that redundancy?
An elections operations (non-partisan) data team must shrink a parcel table before a costly GPU training run while preserving land-use mix. Which sampling approach is most appropriate?
A municipal fleet telematics desk wants a quick EDA sample of 311 tickets without skewing open versus closed status rates. What sampling practice best preserves those proportions?
A solid-waste route optimization analytics team will cluster library branch usage patterns and must cut near-constant columns first. Which step best controls dimensionality before clustering?
A county social-services case analytics team must downsample dense GPS pings from mobile caseworker routes while keeping peak-hour coverage. Which sampling design best meets that goal?
A city planning zoning analytics team must shrink a wide feature set for a model that council staff must interpret. When should they prefer feature selection over PCA?
A city open-data portal needs cleaned assessor tables stored for repeated GPU analytics reads. Which storage choice best supports efficient columnar access?
A county public-health analytics team stores multi-year case extracts and usually queries only the current fiscal year on GPU. How should Parquet be laid out to help scans skip unused years?
A transit authority ridership desk is landing inspection archives into the municipal data lake and needs smaller footprint without abandoning analytics-friendly layout. What should they prioritize?
A water-utility sensor analytics ETL currently writes intermediate stages to CSV between GPU steps even though schemas are stable. What change most improves pipeline efficiency?
An emergency-management fusion cell must add a new permit-attribute column to existing Parquet datasets used by multiple GPU jobs. What practice best manages that schema evolution?
A housing-authority waitlist analytics job only needs a few columns and recent rows from a large Parquet dataset on GPU. Which read strategy best limits device memory pressure?