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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A) Int64
B) Float64
C) String
D) Int32

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) Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
C) Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
D) Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.

3. 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) Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
D) Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.

4. You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

A) Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
B) Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
C) Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
D) Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.

5. A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A) Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
B) Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
C) Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
D) Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.

Solutions: