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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A. Z-score (Standard Score) Analysis
B. One-Hot Encoding
C. Moving Average Smoothing
D. Principal Component Analysis (PCA)

Question 2

You are working on a financial fraud detection system using NVIDIA RAPIDS cuML. You have a large time-series dataset of transaction amounts over time, and you need to identify anomalies such as unusual spikes in transactions.
Which of the following approaches is the most appropriate for detecting anomalies using NVIDIA technologies?

A. Apply cuML's PCA-based anomaly detection to detect outliers in the principal component space
B. Run standard Pandas-based anomaly detection methods on CPU for better precision
C. Train a deep learning model with TensorFlow and deploy it using NVIDIA Triton Inference Server for anomaly detection
D. Use cuML's DBSCAN clustering to identify anomalies based on density differences

Question 3

You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.
B. Reduce the number of Dask workers to minimize parallel execution overhead.
C. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
D. Switch to using Pandas with Dask to compare execution speed differences.

Question 4

Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A. Business Understanding
B. Data Preparation
C. Model Building
D. Data Understanding

Question 5

A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A. Use a cloud instance with multiple GPUs and enable mixed-precision training.
B. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
C. Store all training data in RAM and load it directly to the CPU for processing.
D. Increase the number of CPU cores and distribute training across multiple CPU threads.

Solutions: