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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
B) Use Pandas directly for data manipulation and visualization.
C) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
D) Perform all EDA using NumPy and SciPy for optimized array computations.

2. You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A) Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.
B) Run the pipeline on CPU instead of GPU to compare execution times.
C) Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
D) Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.

3. You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A) Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
B) Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
C) Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
D) Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
E) Multi-GPU setups perform better only when the batch size is reduced.

4. A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A) Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
B) Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.
C) Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
D) Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.

5. Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A) Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
B) Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.
C) Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.
D) Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.

Solutions: