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

2. A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A) Increase the number of CPU cores and distribute training across multiple CPU threads.
B) Use a cloud instance with multiple GPUs and enable mixed-precision training.
C) Store all training data in RAM and load it directly to the CPU for processing.
D) Disable CUDA and use only OpenMP to parallelize computations across CPU cores.

3. A financial institution is using cuGraph to analyze transaction data and detect potential fraudulent activity. The institution wants to identify users who have a high likelihood of being involved in suspicious activities based on the structure of their transactions.
Which of the following cuGraph algorithms would be the best choice for this task?

A) Spectral Clustering
B) Weakly Connected Components
C) Breadth-First Search (BFS)
D) Betweenness Centrality

4. A team of data engineers is working on an Apache Spark-based distributed computing pipeline that leverages NVIDIA GPUs and RAPIDS. They notice that shuffle operations are causing significant slowdowns in performance.
Which optimization strategy should they implement to reduce shuffle impact?

A) Use Spark's default shuffle partitioning without any modification, as GPUs inherently optimize shuffle operations.
B) Disable GPU memory caching to allow automatic CPU-based shuffle optimization.
C) Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.
D) Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.

5. You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A) Reduce training epochs significantly to save time, even if the model is underfitting
B) Test different model configurations on a CPU first before moving to the GPU for final evaluation
C) Train each possible model variation from scratch to evaluate accuracy and performance differences
D) Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training

Solutions: