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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

A) Storing data directly on an HDD and processing with GPUs
B) Leveraging Dask for distributed GPU-based parallel computing
C) Using cuDF for GPU-accelerated DataFrame operations
D) Using TensorFlow with custom data pipelines for data loading

2. You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A) Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
B) Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
C) Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.
D) Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.

3. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A) Z-score Standardization
B) Min-Max Scaling
C) Log Transformation
D) Robust Scaling

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

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

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