Foundations of Accelerated Data Science

NCA-ADS · 24 questions

• A county assessor parcel desk needs quick KPI math on valuation arrays inside a Jupyter notebook. Which foundational approach fits best?
• A 311/resident-service analytics team wants to prototype transforms on a sample before running the full GPU job. What foundational practice should they use?
• A permitting and inspections bureau wants Jupyter work that auditors can follow. How should notebook cells be organized?
• A fire/EMS response-time analytics team is stuck looping over a Python list of dicts for every calculation. What should they do early?
• A public hospital district throughput desk must explain to leadership why GPUs help on large tabular transforms. Which explanation is most accurate?
• An elections operations (non-partisan) data team needs the GPU counterparts to familiar pandas and scikit-learn stacks. What should they identify?
• A municipal fleet telematics desk has a tiny daily CSV that already meets the SLA on CPU. What is the sound foundational judgment?
• A solid-waste route optimization analytics team finished overnight ETL on GPU and must explain why to public works. Which reason best fits?
• A county social-services case analytics pipeline copies frames between CPU and GPU after nearly every transform. What should they optimize?
• A city planning zoning analytics team already paid the cost to load a large fact table onto the GPU. What should they do next for feature engineering?
• A city open-data portal must join a tiny control-code table to a huge GPU-resident fact table. How should they match workloads to hardware?
• A county public-health analytics team profiles a 'GPU' pipeline and finds most time is spent copying data to and from the device. What diagnosis fits?
• A transit authority ridership desk is onboarding a brand-new dataset. Which foundational workflow order should they follow before model fitting?
• A water-utility sensor analytics lead is asked to train immediately on raw dumps that skipped cleaning and transform gates. What should they do?
• An emergency-management fusion cell needs many departments to reuse the same prepared features. What should the transform stage produce?
• A housing-authority waitlist analytics team keeps failing the same clean rules because source fields arrive mis-typed. Where should they look first in the workflow?
• A courts / clerk e-filing analytics workload fits comfortably in memory on one well-equipped GPU host. Which framework choice fits best?
• A library program attendance analytics team finds one GPU host cannot hold countywide fact tables. What complementary approach should they consider?
• A parks and recreation demand desk assumes any Spark cluster is automatically GPU-accelerated. What correction should they make?
• A school-district enrollment analytics team must meet a municipal runtime SLA with the least operational complexity. How should they choose frameworks?
• A county assessor parcel desk is training a GPU XGBoost valuation pilot and must decide which knobs count as model parameters needing deliberate tuning. What should they treat as parameters to tune?
• A 311/resident-service analytics pilot shows very low training error but much higher validation error on holdout tickets. How should the team label this pattern?
• A permitting and inspections bureau sees high error on both the training set and the validation set for a risk-scoring model. What diagnosis and remedy fit best?
• A fire/EMS response-time analytics team keeps adding depth and features, but validation gains have flattened while complexity keeps rising. What should they do?