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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A) Limit the benchmark to small datasets since GPUs excel at parallel processing.
B) Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
C) Focus only on processing speed without considering resource consumption differences between frameworks.
D) Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.

2. A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?

A) Use CPU-based data augmentation techniques before transferring the data to the GPU for training.
B) Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.
C) Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.
D) Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.

3. You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

A) NVIDIA Nsight Compute to optimize image dataset processing.
B) NVIDIA Triton Inference Server to preprocess the dataset before model training.
C) NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
D) NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.

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

5. A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A) Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
B) Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
C) Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.
D) Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.

Solutions: