NCP-ADS Practice Questions: Data Manipulation and Software Literacy Domain

Published: July 30, 2025 | 20 min read

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

NCP-ADS Practice Questions

Master the Data Manipulation and Software Literacy Domain

Test your knowledge in the Data Manipulation and Software Literacy 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

During a data manipulation task, you notice your GPU memory usage is very high. Which of the following actions would help reduce memory usage?

A) Use pandas instead of cuDF

B) Enable mixed precision

C) Use cuDF with smaller data types

D) Switch to a CPU-based computation

Show Answer & Explanation

Correct Answer: C

Explanation: Using cuDF with smaller data types can reduce memory usage by allocating less space for each element. Mixed precision is more relevant to deep learning tasks. Using pandas or switching to CPU-based computation would not leverage GPU acceleration, which is the goal here.

Question 2

You are tasked with processing a large dataset using the RAPIDS cuDF library. The dataset contains missing values and needs to be cleaned before analysis. Which cuDF method would you use to efficiently fill missing values with the mean of each column on a multi-GPU setup?

A) df.fillna(value=0)

B) df.mean().to_dict()

C) df.fillna(df.mean())

D) df.dropna()

Show Answer & Explanation

Correct Answer: C

Explanation: Option C is correct because 'df.fillna(df.mean())' is a method in cuDF that fills missing values with the mean of each column. This is efficient for handling missing data in large datasets on a GPU. Option A fills missing values with zeros, which is not what is required here. Option B calculates the mean but does not fill missing values. Option D removes rows with missing values, which may not be desirable if you want to retain as much data as possible.

Question 3

Which RAPIDS library would you use to perform graph analytics on a large dataset?

A) cuDF

B) cuML

C) cuGraph

D) cuPy

Show Answer & Explanation

Correct Answer: C

Explanation: cuGraph is the RAPIDS library specifically designed for graph analytics, offering GPU-accelerated algorithms for tasks such as PageRank and community detection. cuDF is for DataFrame operations, cuML for machine learning, and cuPy for array computations.

Question 4

Which cuDF operation is most appropriate for combining two DataFrames based on a common key?

A) concat()

B) merge()

C) join()

D) groupby()

Show Answer & Explanation

Correct Answer: B

Explanation: The merge() operation in cuDF is used to combine two DataFrames based on a common key, similar to SQL joins. concat() is used for concatenating DataFrames along a particular axis. join() is similar to merge() but is specifically a method of DataFrame objects. groupby() is used for grouping data, not for combining DataFrames.

Question 5

When transforming data with cuDF, which operation can be used to concatenate two DataFrames along the columns?

A) cuDF.concat(axis=1)

B) cuDF.merge

C) cuDF.join

D) cuDF.append

Show Answer & Explanation

Correct Answer: A

Explanation: cuDF.concat(axis=1) is used to concatenate DataFrames along the columns. Merge and join are used for combining DataFrames based on keys, and append is used to add rows to a DataFrame.

Question 6

For inference scaling across multiple nodes using NVIDIA Triton Inference Server, which is a best practice?

A) Deploy a single instance of Triton on one node.

B) Use Triton's built-in model ensemble feature.

C) Deploy multiple instances of Triton across nodes with a load balancer.

D) Run Triton in a non-containerized environment.

Show Answer & Explanation

Correct Answer: C

Explanation: Deploying multiple instances of Triton across nodes with a load balancer is a best practice for scaling inference. This setup ensures efficient distribution of inference requests and maximizes resource utilization. A single instance or non-containerized deployment would not scale effectively.

Question 7

Your Dask cluster is experiencing memory issues during a large-scale data transformation. What is a potential solution to mitigate this problem?

A) Increase the number of worker threads per GPU.

B) Reduce the chunk size of the Dask DataFrame.

C) Disable Dask's memory spill-to-disk feature.

D) Use a single GPU to handle the entire workload.

Show Answer & Explanation

Correct Answer: B

Explanation: Reducing the chunk size of the Dask DataFrame can help manage memory usage by distributing the workload more evenly across available resources. Increasing worker threads might exacerbate memory issues, and disabling spill-to-disk would remove an important memory management feature. Using a single GPU would not leverage the cluster's resources effectively.

Question 8

Which of the following is a key advantage of using cuDF over pandas for data manipulation in GPU-accelerated environments?

A) cuDF supports more data types than pandas

B) cuDF automatically scales across multiple nodes

C) cuDF operations are optimized for parallel execution on GPUs

D) cuDF requires less setup than pandas

Show Answer & Explanation

Correct Answer: C

Explanation: cuDF operations are optimized for parallel execution on GPUs, providing significant performance improvements in GPU-accelerated environments. While cuDF does not automatically scale across nodes (Dask is used for that), it does not inherently support more data types than pandas, and setup requirements are similar.

Question 9

What is a key advantage of using RAPIDS cuDF over pandas for data manipulation in a GPU-accelerated environment?

A) cuDF supports more data types than pandas

B) cuDF operations are optimized for GPU execution

C) cuDF provides built-in visualization tools

D) cuDF automatically scales data across multiple GPUs

Show Answer & Explanation

Correct Answer: B

Explanation: The key advantage of using RAPIDS cuDF over pandas is that cuDF operations are optimized for GPU execution, providing significant speedups for large-scale data manipulation tasks. cuDF does not support more data types than pandas, does not provide built-in visualization tools, and does not automatically scale data across multiple GPUs without additional configuration.

Question 10

When profiling a deep learning model with DLProf, which of the following is NOT a focus area?

A) Kernel execution times

B) CPU utilization

C) Data transfer bottlenecks

D) GPU memory allocation

Show Answer & Explanation

Correct Answer: B

Explanation: DLProf primarily focuses on GPU-related metrics such as kernel execution times, data transfer bottlenecks, and GPU memory allocation. CPU utilization is not a primary focus of DLProf, which is designed for GPU profiling.

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About NCP-ADS Certification

The NCP-ADS certification validates your expertise in data manipulation and software literacy 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.