NCP-ADS
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A data scientist needs to run a single quick aggregation on a 5,000-row CSV file, one time only, as part of an ad-hoc exploratory analysis. Which approach is BEST?
For tiny, one-off datasets, the fixed cost of host-to-device transfer and CUDA context initialization exceeds any compute savings from the GPU; pandas on CPU finishes faster and with less overhead.
A team has a 4,000-line pandas ETL script that performs well on CPU but now needs to run faster on a workstation with an NVIDIA GPU. Management wants the speedup with zero code changes to the script itself. Which approach BEST meets this requirement?
cudf.pandas is the zero-code-change GPU accelerator for pandas. Launching a script with `python -m cudf.pandas script.py` (or `%load_ext cudf.pandas` in a notebook) proxies pandas calls to cuDF on GPU-supported operations and falls back to pandas/CPU automatically for unsupported ones, requiring no source modification.
A data science team must process a 500GB CSV dataset on a workstation with a single 32GB GPU. The transformations include groupby aggregations and joins. Which approach BEST satisfies this requirement?
dask-cuDF splits the dataset into partitions sized to fit in GPU memory and can spill excess partitions to host memory, enabling correct out-of-core processing of joins and groupbys far larger than a single GPU's memory.
A payment processor wants to identify rings of colluding accounts that pass money back and forth in circular patterns to launder funds. Which approach BEST fits this requirement?
Circular, colluding money-laundering rings are a relationship pattern, not a per-account statistical or geographic anomaly. Modeling accounts/transactions as a graph and applying cuGraph community detection (Louvain) surfaces tightly-connected clusters indicative of collusion.
A data science team has a dataset of 50 million rows that fits entirely within a single A100 GPU's 80GB memory. They need to perform density-based clustering to identify arbitrarily shaped clusters and flag outliers, without specifying the number of clusters in advance. Which cuML algorithm BEST meets these requirements?
cuML DBSCAN is a GPU-accelerated density-based clustering algorithm that discovers arbitrarily shaped clusters and natively flags noise points as outliers, without requiring a predefined cluster count.
A data scientist notices via Nsight Systems that a significant portion of pipeline time is consumed by cudaMemcpy calls between host and device, even though the workflow uses cuDF for all transformations. Which change would BEST reduce this overhead?
Keeping intermediate results as cuDF/cuML GPU objects avoids round trips to host memory, which is the root cause of the memcpy overhead shown in the profile.
Which RAPIDS library provides GPU-accelerated graph analytics algorithms such as PageRank and breadth-first search (BFS)?
cuGraph is the RAPIDS library dedicated to GPU-accelerated graph analytics, implementing algorithms like PageRank, BFS, connected components, and Louvain community detection.
An engineer runs `df['phone'].str.extract(r'(?<!\d)(\d{3}-\d{4})')` on a cuDF Series and it raises a regex compilation error, even though the identical pattern works fine on the same column in pandas. What is the MOST likely cause?
cuDF's GPU string/regex engine (built on libcudf, not Python's `re`) supports a subset of regex syntax and does not support lookbehind/lookahead assertions among other advanced constructs. Patterns using these features that work in pandas will fail or behave differently in cuDF.
A single server has 4 NVIDIA A100 GPUs. The team wants to accelerate a large ETL pipeline consisting of filtering, joins, and groupby operations across all 4 GPUs. Which setup is the correct choice?
LocalCUDACluster spins up one Dask worker per GPU on a single node, letting dask-cuDF distribute DataFrame operations across all 4 A100s in parallel.
You need to rank nodes in a large web-crawl graph by importance based on the structure of incoming links, where a link from an important page counts more than one from an unimportant page. Which cuGraph algorithm should you use?
PageRank ranks nodes by importance based on the quantity and quality of incoming links, recursively weighting links from important nodes more heavily — exactly the described requirement.
A team wants to train an XGBoost 2.x model using GPU acceleration for gradient boosted tree construction. Which configuration correctly enables GPU-based training?
XGBoost 2.0+ deprecated the legacy 'gpu_hist' tree method in favor of tree_method='hist' combined with the device='cuda' parameter to explicitly select GPU-accelerated training.
A Dask-cuDF workflow processing a 200GB dataset on a cluster of GPUs with 32GB memory each begins spilling data to host memory partway through execution, causing a 5x slowdown. Nsight Systems shows large gaps where GPUs are idle waiting on host-to-device copies. What is the BEST first optimization to try?
An RMM pool allocator with a tuned device_memory_limit reduces fragmentation and gives predictable control over when host spilling occurs, directly addressing the GPU memory pressure described without abandoning GPU acceleration.
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How much does the NCP-ADS exam cost?
The exam fee is approximately $200 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 6 domains: GPU-Accelerated Data Science Fundamentals (~18%), Data Preparation and Manipulation with cuDF (~22%), ETL and Distributed Processing (~20%), Advanced Analytics (~18%), Model Training and Acceleration (~12%), Best Practices, Profiling, and Optimization (~10%). The full course has a dedicated chapter, lab and practice-test coverage for each.
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Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $34.99 (list $129.99) through September 9 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.