NVIDIA NCP-ADS : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A) Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
B) Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
C) Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
D) Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

2. Which of the following best describes the purpose of the NVIDIA TensorRT library?

A) Accelerates training of neural networks
B) Provides hardware abstraction for AI model development
C) Manages GPU resources for deep learning models
D) Optimizes and accelerates inference of trained models

3. You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A) Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
B) Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.
C) Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
D) Compare training times only without considering throughput, power efficiency, or memory utilization.

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

5. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
B) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
C) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
D) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool

Solutions: