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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

A) Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker
B) Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads
C) Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration
D) Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF
E) Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs

2. After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A) Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.
B) Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
C) Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
D) Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.

3. You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A) Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
B) Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.
C) Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
D) Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.

4. A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A) Storing and retrieving models from a centralized object storage system.
B) Training and inference workloads using deep learning models with TensorFlow or PyTorch.
C) Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
D) Model monitoring by logging metadata and performance metrics in a database.

5. You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A) Load the dataset into Pandas and use Matplotlib for visualization
B) Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter
C) Use TensorFlow and Keras to preprocess the data before performing EDA
D) Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration

Solutions: