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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A) Using mixed-precision training with Tensor Cores
B) Running training exclusively on CPU
C) Disabling data parallelism
D) Prefetching data with the CPU while training on the GPU

2. Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

A) Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
B) Training a large-scale natural language processing (NLP) model on text data with billions of words.
C) Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
D) Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
E) Running a deep learning model for image classification with millions of labeled images.

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

4. You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
B) Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
C) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
D) Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format

5. You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A) Spark with RAPIDS Accelerator
B) cuDF
C) pandas
D) Dask DataFrame with Dask-CUDA

Solutions: