NCP-ADS Practice Questions: MLOps Domain

Published: July 30, 2025 | 20 min read

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

NCP-ADS Practice Questions

Master the MLOps Domain

Test your knowledge in the MLOps 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 using a CI/CD pipeline for model deployment. What is a best practice to follow when integrating model testing into the pipeline?

A) Skip testing to expedite deployment.

B) Include automated tests for model performance and accuracy.

C) Test models only after deployment.

D) Rely solely on manual testing by data scientists.

Show Answer & Explanation

Correct Answer: B

Explanation: Including automated tests for model performance and accuracy in the CI/CD pipeline ensures that models meet the required standards before deployment. This practice helps catch potential issues early and maintains the quality of deployed models.

Question 2

To scale your model inference across multiple GPUs using NVIDIA Triton, what is an essential configuration step?

A) Ensure the model is trained with mixed precision.

B) Configure the Triton server with a single GPU setting.

C) Use Triton's dynamic batching feature.

D) Deploy multiple instances of the model, one per GPU.

Show Answer & Explanation

Correct Answer: C

Explanation: Triton's dynamic batching feature allows for efficient utilization of multiple GPUs by aggregating inference requests. This is crucial for scaling inference across multiple GPUs. Deploying multiple instances is not necessary with dynamic batching, and single GPU settings would not utilize multiple GPUs effectively.

Question 3

Which practice is crucial for ensuring the scalability of a machine learning model in a cloud environment using NVIDIA technologies?

A) Deploying models without any form of containerization.

B) Using GPU memory optimization techniques.

C) Running all models on a single node to reduce complexity.

D) Avoiding the use of RAPIDS libraries to maintain simplicity.

Show Answer & Explanation

Correct Answer: B

Explanation: Using GPU memory optimization techniques is crucial for ensuring that models can scale efficiently in a cloud environment, particularly when utilizing NVIDIA technologies. Containerization is important for scalable deployments, while running on a single node and avoiding RAPIDS would limit scalability and performance.

Question 4

A deployed model in production is experiencing degraded performance. Which tool can you use to profile and diagnose the model's GPU usage?

A) cuDF

B) DLProf

C) cuGraph

D) Dask

Show Answer & Explanation

Correct Answer: B

Explanation: DLProf is a profiling tool designed to analyze the performance of deep learning models on NVIDIA GPUs. It helps identify bottlenecks and inefficiencies in GPU usage, making it suitable for diagnosing performance issues in deployed models.

Question 5

During a production deployment, your model's inference time suddenly spikes. Which NVIDIA tool can help profile and diagnose performance bottlenecks?

A) cuDF

B) DLProf

C) cuML

D) cuGraph

Show Answer & Explanation

Correct Answer: B

Explanation: DLProf is a profiling tool provided by NVIDIA specifically designed to help diagnose and optimize the performance of deep learning models. It provides insights into time spent on different operations, helping identify bottlenecks. cuDF, cuML, and cuGraph are part of the RAPIDS suite for data manipulation and analytics, not profiling.

Question 6

In a CI/CD pipeline for a GPU-accelerated data science project, which of the following is a best practice to ensure efficient resource utilization?

A) Run all tests sequentially on a single GPU.

B) Use Dask to parallelize tests across multiple GPUs.

C) Deploy models to production without testing.

D) Avoid using containers to reduce overhead.

Show Answer & Explanation

Correct Answer: B

Explanation: Using Dask to parallelize tests across multiple GPUs ensures efficient resource utilization by distributing the workload. Running tests sequentially on a single GPU (A) is not efficient, and deploying models without testing (C) is risky. Containers (D) provide consistency and isolation, which are important in CI/CD pipelines.

Question 7

In a production environment, which technique is recommended for updating a deployed model with minimal downtime?

A) Perform a rolling update using Kubernetes.

B) Stop the current model, update it, then restart.

C) Deploy the new model on a different server and switch traffic manually.

D) Update the model during off-peak hours.

Show Answer & Explanation

Correct Answer: A

Explanation: A rolling update using Kubernetes allows for updating a deployed model with minimal downtime by incrementally replacing instances of the application with new ones. This ensures continuous availability. Stopping the model or switching traffic manually can cause downtime, and updating during off-peak hours does not guarantee zero downtime.

Question 8

For a production scaling scenario where inference latency is critical, which strategy should be employed with NVIDIA Triton Inference Server?

A) Deploy multiple instances of the server with model replicas.

B) Use a single high-performance GPU to handle all requests.

C) Disable dynamic batching to reduce processing complexity.

D) Limit the number of concurrent requests to minimize load.

Show Answer & Explanation

Correct Answer: A

Explanation: Deploying multiple instances with model replicas allows for load balancing and redundancy, improving inference latency. Option B might lead to bottlenecks. Option C would increase latency as dynamic batching can optimize request processing. Option D would unnecessarily limit throughput.

Question 9

To ensure reproducibility in your MLOps pipeline, which practice should be prioritized?

A) Using hardcoded paths for data sources.

B) Documenting all manual steps in a README file.

C) Versioning both the code and the data.

D) Excluding configuration files from version control.

Show Answer & Explanation

Correct Answer: C

Explanation: Versioning both the code and the data is crucial for reproducibility, as it ensures that all components of the pipeline can be recreated exactly as they were at any point in time. Hardcoded paths and excluding configuration files can lead to inconsistencies, while manual documentation is not as reliable as automated version control.

Question 10

In a production environment, you observe that the inference latency of your model has increased. Which approach is recommended to diagnose and resolve this issue using NVIDIA technologies?

A) Increase the batch size without profiling the model.

B) Use DLProf to profile the model and identify bottlenecks.

C) Switch to CPU inference to reduce GPU load.

D) Add more GPUs without analyzing the current setup.

Show Answer & Explanation

Correct Answer: B

Explanation: Using DLProf (B) to profile the model helps identify specific bottlenecks in the inference process, allowing for targeted optimizations. Increasing batch size (A) or adding more GPUs (D) without understanding the issue might not address the root cause. Switching to CPU inference (C) typically increases latency rather than reduces it.

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About NCP-ADS Certification

The NCP-ADS certification validates your expertise in mlops 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.