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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A) Use CPU-based implementations of the same frameworks for comparison.
B) Benchmark only the training phase of the deep learning model.
C) Use a common dataset for all frameworks to ensure comparability.
D) Use a batch size that is optimal for each framework's memory management.
E) Measure GPU utilization and memory usage, but ignore the network and disk I/O.

2. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
B) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
C) Load the data into a Spark DataFrame and visualize using Apache Zeppelin
D) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly

3. You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A) Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.
B) Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
C) Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
D) Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.

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

5. Which of the following best describes the purpose of the NVIDIA TensorRT library?

A) Accelerates training of neural networks
B) Optimizes and accelerates inference of trained models
C) Manages GPU resources for deep learning models
D) Provides hardware abstraction for AI model development

Solutions: