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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following are key advantages of using cuGraph for analyzing graph data in GPU- accelerated environments? (Select two)

A) cuGraph does not support distributed graph processing and is only suitable for single-node systems.
B) cuGraph only works on small-scale graph datasets that can fit into memory.
C) cuGraph supports various graph algorithms, including PageRank, shortest path, and community detection, leveraging GPU parallelism.
D) cuGraph can efficiently handle larger graphs than traditional CPU-based methods, providing significant performance improvements.
E) cuGraph only works with cloud-based computing environments and is not optimized for local GPUs.

2. You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A) Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
B) Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
C) Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
D) Using a different precision setting for each framework to maximize performance per framework's capabilities.

3. You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A) Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
B) Install all required packages globally on the system using pip install without a virtual environment.
C) Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.
D) Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.

4. A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A) Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
B) Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
C) Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
D) Remove all rows with missing values and outliers to ensure only clean data is used.

5. You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A) Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
B) Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
C) Store all data as CSV files and perform ETL operations using traditional row-based processing.
D) Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.

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