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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is working with datasets ranging from hundreds of megabytes to several terabytes and needs to select the most efficient NVIDIA-accelerated data processing library for optimal memory management and performance.
Which approach is best for selecting the appropriate library for different dataset sizes?

A) Use Pandas for all dataset sizes since Pandas has built-in multi-threading optimizations.
B) Use cuDF for small datasets and Dask-cuDF for larger datasets that do not fit in a single GPU's memory.
C) Use RAPIDS cuML for handling all data processing tasks, regardless of dataset size.
D) Always use Dask regardless of dataset size since Dask automatically scales from small to large datasets.

2. You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A) Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
B) Using TensorFlow's Dataset API to load and preprocess the data on GPUs
C) Using Apache Spark with PySpark for CPU-based distributed data processing
D) Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs

3. You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A) df['transaction_id'] = df['transaction_id'].astype('int32')
B) df['transaction_id'] = df['transaction_id'].astype('float32')
C) df['transaction_id'] = df['transaction_id'].astype('int64')
D) df['transaction_id'] = df['transaction_id'].astype('int8')

4. A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A) Storing all training data in GPU memory at once
B) Using mixed precision training with automatic loss scaling
C) Increasing batch size without adjusting the optimizer settings
D) Using single-precision (FP32) calculations for better accuracy

5. You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A) 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
B) 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
C) 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
D) 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')