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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A) Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
B) Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
C) Use Dask's default scheduler to distribute work across GPUs without any specific configuration
D) Run the computation on a single GPU and let Dask handle CPU parallelism automatically
E) Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing

2. You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A) Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
B) Install NVIDIA drivers manually inside a Docker container every time it runs.
C) Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
D) Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.

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

4. A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?

A) Python multiprocessing module
B) Apache Spark with RAPIDS Accelerator
C) SQLite
D) Pandas

5. You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A) Use cuDF's .fillna() method to replace missing values in GPU memory
B) Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
C) Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
D) Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

Solutions: