NCP-ADS Practice Questions: GPU and Cloud Computing Domain

Published: July 30, 2025 | 20 min read

Test your NCP-ADS knowledge with 10 practice questions from the GPU and Cloud Computing domain. Includes detailed explanations and answers.

NCP-ADS Practice Questions

Master the GPU and Cloud Computing Domain

Test your knowledge in the GPU and Cloud Computing 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

You are experiencing memory issues with your Dask cluster in the cloud. What is a potential solution to address this problem?

A) Increase the number of CPU cores in each worker node.

B) Reduce the size of the dataset being processed.

C) Add more worker nodes with additional GPU resources.

D) Switch to a single-node setup to simplify memory management.

Show Answer & Explanation

Correct Answer: C

Explanation: Adding more worker nodes with additional GPU resources can help alleviate memory issues by distributing the workload more effectively across the cluster. Options A, B, and D do not directly address the need for increased memory capacity and parallel processing capabilities.

Question 2

You are tasked with performing graph analytics on a large social network dataset using cuGraph in the cloud. Which of the following is a key advantage of using cuGraph for this task?

A) cuGraph provides built-in support for cloud-native deployment.

B) cuGraph leverages GPU acceleration to handle large-scale graph processing efficiently.

C) cuGraph is optimized for CPU-based processing, making it suitable for cloud environments.

D) cuGraph automatically scales across multiple cloud instances without configuration.

Show Answer & Explanation

Correct Answer: B

Explanation: cuGraph is designed to leverage GPU acceleration, which makes it highly efficient for processing large-scale graph data. While it can be deployed in the cloud, its primary advantage is the GPU acceleration, not automatic cloud scaling or CPU optimization.

Question 3

You have a large graph dataset stored in a cloud environment and need to perform analytics using cuGraph. What is the first step you should take to ensure efficient processing?

A) Transfer the entire dataset to a single GPU.

B) Utilize Dask to distribute the dataset across multiple GPUs.

C) Convert the dataset to a pandas DataFrame.

D) Run analytics directly on the cloud storage without loading data.

Show Answer & Explanation

Correct Answer: B

Explanation: Using Dask to distribute the dataset across multiple GPUs allows you to leverage parallel processing capabilities, which is essential for handling large datasets efficiently in a cloud environment. Transferring the entire dataset to a single GPU (Option A) would likely exceed memory limits. Converting to a pandas DataFrame (Option C) is not optimal for GPU processing, and running analytics directly on cloud storage (Option D) without loading data is not feasible.

Question 4

When deploying a GPU-accelerated data science application in a cloud environment, which tool is essential for managing container dependencies to ensure consistency across different platforms?

A) Docker

B) Kubernetes

C) TensorFlow

D) Apache Spark

Show Answer & Explanation

Correct Answer: A

Explanation: Docker is a tool designed to create, deploy, and run applications by using containers. It ensures that the application works in the same way regardless of where it is deployed. Kubernetes (B) is used for container orchestration, TensorFlow (C) is a machine learning framework, and Apache Spark (D) is a data processing engine.

Question 5

Which cuDF operation is typically used to efficiently transform and clean large datasets in a cloud-based GPU environment?

A) cuDF.dropna()

B) cuDF.fillna()

C) cuDF.merge()

D) cuDF.concat()

Show Answer & Explanation

Correct Answer: A

Explanation: cuDF.dropna() is used to remove missing values from datasets, which is a common data cleaning operation. While cuDF.fillna() is also used for handling missing values, dropna() is specifically for removing them. cuDF.merge() and cuDF.concat() are used for combining datasets, not cleaning.

Question 6

You have a cloud-based application that uses cuGraph for graph analytics. How can you benchmark its performance to ensure it meets your needs?

A) Use a cloud provider's default benchmarking tools.

B) Implement custom logging to track execution times.

C) Utilize RAPIDS' built-in benchmarking utilities for accurate performance measurement.

D) Rely on manual timing of operations within the application code.

Show Answer & Explanation

Correct Answer: C

Explanation: RAPIDS provides built-in benchmarking utilities that are specifically designed to measure the performance of GPU-accelerated applications like cuGraph. These tools offer more accurate and relevant insights compared to general cloud provider tools or manual timing.

Question 7

You have a multi-GPU setup in the cloud for training a deep learning model. Which strategy should you employ to ensure efficient resource utilization and faster training times?

A) Use data parallelism to split the dataset across GPUs.

B) Increase the batch size to fit the entire dataset on one GPU.

C) Use a single GPU and leave others idle for backup.

D) Run separate training sessions on each GPU with different hyperparameters.

Show Answer & Explanation

Correct Answer: A

Explanation: Data parallelism involves splitting the dataset across multiple GPUs, allowing each GPU to process a portion of the data concurrently. This strategy maximizes resource utilization and reduces training time. Options B, C, and D do not leverage the multi-GPU setup effectively.

Question 8

Which component of the RAPIDS ecosystem is specifically designed for performing graph analytics on GPUs?

A) cuDF

B) cuML

C) cuGraph

D) cuPy

Show Answer & Explanation

Correct Answer: C

Explanation: cuGraph is the component of the RAPIDS ecosystem specifically designed for performing graph analytics on GPUs. cuDF is for data manipulation, cuML is for machine learning, and cuPy is for array computing.

Question 9

When managing dependencies in a GPU-accelerated data science project, why might you choose Conda over Docker?

A) Conda provides better GPU support.

B) Conda allows for more granular control over Python package versions.

C) Conda is easier to deploy on cloud platforms.

D) Conda automatically scales applications across multiple GPUs.

Show Answer & Explanation

Correct Answer: B

Explanation: Conda allows for more granular control over Python package versions and dependencies, making it a preferred choice for managing environments in Python-centric projects. Docker (Option A) is generally better for GPU support due to containerization, and it is also easier to deploy on cloud platforms (Option C). Conda does not handle scaling across multiple GPUs (Option D).

Question 10

When deploying a machine learning model using NVIDIA Triton Inference Server in the cloud, what is a key consideration to ensure scalable and efficient deployment?

A) Deploy the model on a single GPU to simplify management.

B) Use mixed precision to reduce the model's computational requirements.

C) Configure model replicas to handle increased load.

D) Optimize the model for CPU inference to reduce cloud costs.

Show Answer & Explanation

Correct Answer: C

Explanation: Configuring model replicas in NVIDIA Triton Inference Server allows the system to handle increased load by distributing requests across multiple instances. This ensures scalability and efficient resource utilization. Mixed precision is beneficial for training, not specifically for inference scaling.

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About NCP-ADS Certification

The NCP-ADS certification validates your expertise in gpu and cloud computing 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.