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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

A) Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.
B) Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.
C) Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
D) Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.

2. A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

A) Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
B) Convert all numerical features to float64 to maximize precision for feature transformation.
C) Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
D) Convert all categorical variables to string format for efficient processing in cuDF.

3. Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A) GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
B) GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
C) GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
D) GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.

4. You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A) Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
B) Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
C) Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
D) Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.

5. You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A) Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
B) Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
C) Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
D) Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs

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