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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A) Implementing model performance tracking with drift detection metrics
B) Using a static dataset for monitoring instead of real-time production data
C) Logging only inference latency without tracking model predictions
D) Disabling logging to reduce system overhead and avoid storage costs

3. You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A) Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
B) Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
C) Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
D) Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.

4. 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) Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
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) Increase the number of CPU cores and distribute training across multiple CPU threads.

5. In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A) Using map() function to apply a function element-wise
B) Using for loops to apply operations row by row
C) Using vectorized operations (e.g., element-wise arithmetic)
D) Using apply() function over DataFrame rows
E) Using iterrows() for iterating through DataFrame rows

Solutions: