NCP-ADS Practice Questions: Data Analysis Domain

Published: July 30, 2025 | 20 min read

Test your NCP-ADS knowledge with 10 practice questions from the Data Analysis domain. Includes detailed explanations and answers.

NCP-ADS Practice Questions

Master the Data Analysis Domain

Test your knowledge in the Data Analysis domain with these 10 practice questions. Each question is designed to help you prepare for the NCP-ADS certification exam with detailed explanations to reinforce your learning.

Question 1

How can you optimize GPU memory utilization when working with large datasets in RAPIDS cuDF?

A) Decrease the batch size

B) Use mixed precision

C) Convert data types to more efficient formats

D) Increase the learning rate

Show Answer & Explanation

Correct Answer: C

Explanation: Converting data types to more efficient formats, such as using float32 instead of float64 or int8 instead of int32, can significantly reduce the memory footprint of a dataset in GPU memory. Decreasing batch size and using mixed precision are more relevant to model training, and increasing the learning rate is unrelated to memory utilization.

Question 2

Which technique would you use to detect anomalies in a time-series dataset using GPU acceleration?

A) cuML's DBSCAN

B) cuGraph's PageRank

C) cuDF's dropna

D) cuPy's FFT

Show Answer & Explanation

Correct Answer: A

Explanation: cuML's DBSCAN is a clustering algorithm that can be used for anomaly detection by identifying outliers that don't belong to any cluster. PageRank is for ranking nodes in a graph, dropna is for removing missing data, and FFT is for frequency analysis.

Question 3

You are tasked with performing time-series analysis on a large dataset using GPU acceleration. Which RAPIDS library would you primarily use to efficiently handle and analyze this data?

A) cuML

B) cuGraph

C) cuDF

D) cuPy

Show Answer & Explanation

Correct Answer: C

Explanation: cuDF is a GPU DataFrame library that provides a pandas-like API for handling large datasets efficiently on a GPU. It is suitable for time-series analysis as it allows for rapid data manipulation and analysis, leveraging GPU acceleration.

Question 4

You are tasked with performing anomaly detection on a streaming time-series dataset. Which NVIDIA technology would you employ for real-time analysis?

A) cuGraph

B) NVIDIA Triton Inference Server

C) cuML

D) DLProf

Show Answer & Explanation

Correct Answer: B

Explanation: NVIDIA Triton Inference Server can be used for real-time inference and anomaly detection on streaming data. cuGraph is for graph analytics, cuML is for batch machine learning tasks, and DLProf is for model profiling.

Question 5

In a large-scale data analysis project, you need to perform graph analytics on a dataset with millions of nodes and edges. Which strategy would you employ?

A) Use cuDF to convert the data into a DataFrame for analysis

B) Leverage cuGraph's PageRank algorithm for node importance

C) Apply cuML's PCA for dimensionality reduction

D) Use cuPy for element-wise array operations

Show Answer & Explanation

Correct Answer: B

Explanation: cuGraph's PageRank algorithm is specifically designed for graph analytics and can efficiently handle large-scale node importance calculations. cuDF is for DataFrame manipulations, cuML's PCA is for dimensionality reduction, and cuPy is for array operations.

Question 6

For anomaly detection in a time-series dataset using a sliding window approach, which of the following is a critical parameter to optimize?

A) Window size

B) Data type

C) File format

D) Column names

Show Answer & Explanation

Correct Answer: A

Explanation: The window size is a critical parameter when using a sliding window approach for anomaly detection in time-series data. It determines the number of data points considered at each step and can significantly affect the detection results.

Question 7

While conducting exploratory data analysis on a large dataset using cuDF, you notice performance bottlenecks. Which action is most likely to improve the performance?

A) Increase the number of CPU cores

B) Optimize GPU memory usage

C) Use a smaller dataset

D) Switch to a different programming language

Show Answer & Explanation

Correct Answer: B

Explanation: Optimizing GPU memory usage can improve performance by ensuring that operations are efficiently using available GPU resources, reducing memory transfer times and potential bottlenecks.

Question 8

For graph analytics using GPU acceleration, which RAPIDS library would you utilize?

A) cuML

B) cuGraph

C) cuDF

D) cuPy

Show Answer & Explanation

Correct Answer: B

Explanation: cuGraph is the RAPIDS library specifically designed for graph analytics using GPU acceleration. cuML is for machine learning, cuDF is for data manipulation, and cuPy is a general-purpose array library.

Question 9

What is a primary advantage of using Dask with RAPIDS for time-series analysis?

A) It allows for real-time data streaming

B) It supports distributed computing across multiple GPUs

C) It provides built-in anomaly detection algorithms

D) It simplifies the creation of deep learning models

Show Answer & Explanation

Correct Answer: B

Explanation: Dask enables distributed computing, allowing RAPIDS to scale workloads across multiple GPUs, which is especially beneficial for handling large datasets in time-series analysis. Real-time streaming, built-in anomaly detection, and deep learning model creation are not direct advantages of using Dask with RAPIDS.

Question 10

You have a large dataset of network logs and need to perform anomaly detection using graph analytics. Which NVIDIA RAPIDS library would be most suitable for efficiently analyzing the connectivity and structure of this data?

A) cuDF

B) cuML

C) cuGraph

D) cuPy

Show Answer & Explanation

Correct Answer: C

Explanation: cuGraph is specifically designed for graph analytics and is optimized for GPU acceleration. It allows for efficient analysis of graph structures, which is essential for tasks like anomaly detection in network logs. cuDF is used for general dataframe operations, cuML for machine learning algorithms, and cuPy for numerical computations similar to NumPy.

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About NCP-ADS Certification

The NCP-ADS certification validates your expertise in data analysis and other critical domains. Our comprehensive practice questions are carefully crafted to mirror the actual exam experience and help you identify knowledge gaps before test day.