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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A) Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
B) Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
C) Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
D) Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.

2. A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A) Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.
B) Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
C) Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
D) Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.

3. A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A) Read the Parquet file line by line using Python's built-in file handling functions to save memory.
B) Load the dataset into a relational database and query it using simple SQL statements.
C) Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
D) Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.

4. You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

A) Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.
B) Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.
C) Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.
D) cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.

5. A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?

A) Train a deep learning model on a single GPU first, then switch to Dask for scaling
B) Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
C) Convert all data into Pandas DataFrames before distributing computations
D) Use ThreadPoolExecutor to manage parallel computations on GPUs

Solutions: