NCP-ADS is a decision exam more than a memorization one: which library, which dtype, which tool for this data size. This cheat sheet organizes the RAPIDS stack and the decision rules the exam turns on, for final-week review and as a study checkpoint. For full explanations, the domains breakdown carries the depth and the complete guide covers the exam itself.
Exam Quick Facts
The RAPIDS Library Map
The single most useful thing to memorize: each RAPIDS library and its CPU counterpart.
RAPIDS ↔ PyData equivalents
| RAPIDS (GPU) | CPU equivalent | Does |
|---|---|---|
| cuDF | pandas | DataFrames: ETL, joins, groupby, filter |
| cuML | scikit-learn | ML algorithms with the same fit/predict API |
| cuGraph | NetworkX | Graph analytics: PageRank, community, paths |
| Dask-cuDF | Dask / pandas | Multi-GPU and larger-than-memory DataFrames |
| cuxfilter | (dashboards) | Cross-filtered big-data visualization |
| XGBoost (GPU) | XGBoost (CPU) | Gradient-boosted trees, GPU-accelerated |
If a question names a pandas/sklearn/NetworkX operation and asks for the GPU tool, this table is the answer.
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Library Selection by Dataset Size (Most-Tested Rule)
The GPU is not always the answer. Transfer overhead means small data can be faster on the CPU.
| Dataset size | Use | Why |
|---|---|---|
| Small (fits easily in RAM, ~low millions of rows or less) | pandas | GPU transfer overhead exceeds the compute savings |
| Large, fits in one GPU's VRAM | cuDF | Parallel GPU compute wins decisively |
| Larger than one GPU's memory | Dask-cuDF | Partitions across GPUs / out-of-core |
The trap answer is "always use the GPU." When a question offers pandas, cuDF, and Dask-cuDF, the dataset size in the stem is the deciding factor.
GPU Memory Optimization (The Exam's Recurring Theme)
VRAM is scarce (tens of GB vs hundreds of GB of system RAM), so memory discipline is tested across MLOps, Data Preparation, and Machine Learning at once.
| Technique | What it does |
|---|---|
int64 -> int32, float64 -> float32 | Halves numeric column memory |
string -> category | Huge savings on low-cardinality string columns |
| Batching / chunking | Process data in pieces that fit VRAM |
| Mixed precision (fp16/fp32) | Cuts memory and boosts training throughput |
| Dask-cuDF | Spill and scale when data exceeds one GPU |
Signature exam moment: a workflow hits an out-of-memory error; the fix is almost always a dtype conversion (especially string columns to categoricals) before anything more drastic.
cuML Algorithms
cuML mirrors scikit-learn's API. Know these are GPU-accelerated:
- Regression: linear, logistic, ridge, lasso
- Trees / ensembles: random forest; GPU XGBoost for gradient boosting
- Clustering: k-means, DBSCAN
- Neighbors: k-NN
- Dimensionality reduction: PCA, t-SNE, UMAP
The API is fit() / predict() just like sklearn; the win is speed on large data. GPU XGBoost is the default answer for gradient-boosted trees.
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cuGraph Algorithms
cuGraph by question
| You need to find... | Algorithm |
|---|---|
| Most influential nodes | PageRank |
| Clusters / communities | Community detection (Louvain, Leiden) |
| Shortest route between nodes | BFS / SSSP |
| Most central / connecting nodes | Centrality (betweenness, degree) |
| Connected groups | Connected components |
cuGraph is the RAPIDS answer whenever a scenario involves relationships at a scale NetworkX cannot handle.
Benchmarking GPU vs CPU (Do It Right)
"Prove the GPU is faster" questions reward correct method:
- Warm up first. The first GPU run includes one-time initialization; do not time it.
- Time steady state, ideally averaged over runs.
- Account for data transfer (host-to-device) separately; it is real cost.
- Use data large enough for the GPU to matter; on tiny data the CPU wins and that is expected.
The naive benchmark (cold first run, tiny data, transfer included) makes the GPU look slow, and that is a wrong-answer trap.
Reproducibility & Methodology
- Docker / Conda pin CUDA, drivers, and RAPIDS versions so a workflow runs identically across machines. The "ensure it runs the same on a teammate's GPU" answer.
- CRISP-DM phases in order: Business Understanding -> Data Understanding -> Data Preparation -> Modeling -> Evaluation -> Deployment.
- DLProf profiles deep-learning workflows to locate the real bottleneck before optimizing.
Facts Worth Cold Recall
- cuDF is a near drop-in for pandas; cuML mirrors scikit-learn; cuGraph replaces NetworkX
- Small data can be faster on the CPU; size drives the library choice
- Out-of-memory fixes start with dtype optimization (categoricals, int32/float32)
- Dask-cuDF is for data larger than one GPU's memory
- GPU XGBoost is the go-to for gradient-boosted trees
- Benchmark warm, steady-state, transfer-aware, on large-enough data
- Docker/Conda deliver RAPIDS reproducibility; CRISP-DM frames the workflow
Final-Week Usage
Run this sheet top to bottom and mark anything that produces hesitation, then take a timed practice exam and compare your misses against those marks. Two or three passes and the sheet stops surprising you. Preporato's NCP-ADS practice exams supply the other half: 7 full-length tests, 420 explained questions, and per-domain scoring aligned to the same weights this sheet is organized by.
For sitting strategy, finish with how to pass NCP-ADS on your first attempt.
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Last updated: July 11, 2026
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