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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

A. Model Deployment and Monitoring
B. Business Understanding
C. Model Training and Evaluation
D. Data Understanding
E. Data Exploration

Question 2

You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
B. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.

Question 3

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

Question 4

You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A. Linear Regression
B. Autoencoder-based Anomaly Detection
C. k-Nearest Neighbors (k-NN) for Classification
D. Naive Bayes Classifier

Question 5

A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A. Rely on Matplotlib to generate static plots and update them every minute with a loop.
B. Precompute the time-series visualization with Dask and display it in a static HTML page.
C. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
D. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.

Solutions: