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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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) Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
D) Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.

2. A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A) Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
B) Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
C) Use seaborn with a sampled subset of the dataset to generate line plots.
D) Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.

3. You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A) Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B) Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
C) Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.

4. A data engineer is using cuDF in NVIDIA RAPIDS to generate a large synthetic dataset for machine learning training. The dataset consists of numerical and categorical features. The engineer needs to generate millions of rows efficiently while preserving the relationships between features.
Which of the following approaches is the most optimal?

A) Use cudf.to_pandas(), generate synthetic data using pandas and Scikit-learn, and then convert it back to cuDF.
B) Train a cuML KMeans model on the original data and use the cluster centroids as new synthetic data points.
C) Leverage cuML's PCA.inverse_transform() after fitting PCA to the original dataset to generate new synthetic samples.
D) Use cudf.Series.random() to create independent random values for each column separately.

5. You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A) df['item_id'] = df['item_id'].astype('string')
B) df['item_id'] = df['item_id'].astype('int32')
C) df['item_id'] = df['item_id'].astype('int64')
D) df['item_id'] = df['item_id'].astype('float64')

Solutions: