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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Categorical (grouping transactions into bins such as "low", "medium", "high").
B) Floating-point (with precision to store cents or fractions of a cent).
C) Boolean (True for transactions above $100, False otherwise).
D) Integer (rounding all transaction amounts to whole dollars).

2. You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?

A) df['country'] = df['country'].astype('int32')
B) df['country'] = df['country'].astype('string')
C) df['country'] = df['country'].astype('category')
D) df['country'] = df['country'].astype('object')

3. Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A) cuBLAS
B) Triton Inference Server
C) cuML
D) DLA (Deep Learning Accelerator)

4. A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A) The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
B) The CUDA cores are overheating, leading to automatic throttling of computations.
C) The GPU is not powerful enough to process the deep learning model efficiently.
D) The batch size is too large, leading to excessive GPU memory utilization and slow processing.

5. A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A) Using a single-threaded feature extraction approach to avoid overhead from parallelization.
B) Reducing dataset size by randomly removing data points without considering class balance.
C) Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
D) Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.

Solutions: