NCP-ADS Practice Questions: Data Preparation Domain

Published: July 30, 2025 | 20 min read

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

NCP-ADS Practice Questions

Master the Data Preparation Domain

Test your knowledge in the Data Preparation 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 tasked with cleaning a large dataset using cuDF in preparation for a machine learning model. Which cuDF method would you use to handle missing values effectively?

A) dropna()

B) fillna()

C) replace()

D) isna()

Show Answer & Explanation

Correct Answer: B

Explanation: The fillna() method in cuDF is used to fill missing values with a specified value, which is an effective way to handle missing data before training a machine learning model. dropna() removes rows with missing values, replace() is used for replacing specific values, and isna() is used to identify missing values.

Question 2

When standardizing a dataset with cuDF, which function would you use to ensure each feature has a mean of 0 and a standard deviation of 1?

A) normalize()

B) zscore()

C) standardize()

D) scale()

Show Answer & Explanation

Correct Answer: B

Explanation: The zscore() function is used to standardize a dataset so that each feature has a mean of 0 and a standard deviation of 1. This is critical for many machine learning algorithms that assume the input data is normally distributed. normalize() typically scales data to a range, standardize() is not a direct cuDF function, and scale() is not specific to cuDF.

Question 3

You need to generate synthetic data for testing your machine learning model using RAPIDS. Which library would you primarily use for this task?

A) cuGraph

B) cuML

C) cuDF

D) cuPy

Show Answer & Explanation

Correct Answer: C

Explanation: cuDF is the RAPIDS library that provides functionality for data manipulation, including generating synthetic data. cuGraph is for graph analytics, cuML is for machine learning, and cuPy is a GPU-accelerated library for array computations.

Question 4

In a data pipeline using RAPIDS, you notice a bottleneck during data cleansing operations. What is a recommended approach to monitor and diagnose the issue?

A) Use DLProf to profile the data pipeline

B) Increase the number of GPUs used

C) Implement logging to track data flow

D) Use RAPIDS Memory Manager to optimize memory usage

Show Answer & Explanation

Correct Answer: C

Explanation: Implementing logging to track data flow is a recommended practice to monitor and diagnose bottlenecks in a data pipeline. DLProf is more suited for profiling deep learning models, increasing the number of GPUs may not directly address the bottleneck, and RAPIDS Memory Manager is used for memory optimization, not directly for monitoring.

Question 5

When scaling a data preparation pipeline across multiple GPUs with Dask, what is a common issue that can arise?

A) Data skew

B) Network latency

C) GPU overclocking

D) Insufficient CPU resources

Show Answer & Explanation

Correct Answer: A

Explanation: Data skew, where data is not evenly distributed across partitions, is a common issue when scaling with Dask across multiple GPUs. Network latency can affect distributed systems but is not specific to Dask's GPU scaling. GPU overclocking is unrelated to Dask, and insufficient CPU resources, while possible, is less common in GPU-focused tasks.

Question 6

You are tasked with cleansing a large dataset using cuDF. Which method would you use to efficiently remove rows with null values?

A) dropna()

B) fillna()

C) isnull()

D) replace()

Show Answer & Explanation

Correct Answer: A

Explanation: The dropna() method is used to remove rows with null values in a DataFrame. fillna() is used to fill null values, isnull() is used to check for null values, and replace() is used to replace specific values in a DataFrame.

Question 7

When using cuDF for data standardization, which of the following is a key consideration to ensure consistency across datasets?

A) Ensure all data types are strings

B) Use a consistent scaling method

C) Convert all data to integer type

D) Apply different methods for each column

Show Answer & Explanation

Correct Answer: B

Explanation: Using a consistent scaling method, such as standardization or normalization, ensures that datasets are comparable and consistent in scale. Converting all data to strings or integers is not necessary, and applying different methods for each column can lead to inconsistencies.

Question 8

You need to scale a data preprocessing pipeline across multiple GPUs. Which tool would you use in conjunction with cuDF to achieve this?

A) cuML

B) cuGraph

C) Dask

D) NVIDIA Triton

Show Answer & Explanation

Correct Answer: C

Explanation: Dask is the tool used in conjunction with cuDF to scale data preprocessing pipelines across multiple GPUs. It provides parallel computing capabilities and can distribute workloads efficiently. cuML is for machine learning, cuGraph is for graph analytics, and NVIDIA Triton is for model inference.

Question 9

Which RAPIDS library would you use to monitor a data preparation pipeline for performance bottlenecks?

A) cuGraph

B) cuDF

C) DLProf

D) cuML

Show Answer & Explanation

Correct Answer: C

Explanation: DLProf is a profiling tool in the RAPIDS ecosystem that helps identify performance bottlenecks in data preparation and machine learning pipelines. cuGraph and cuDF are used for graph analytics and data manipulation, respectively, and cuML is for machine learning.

Question 10

You are tasked with monitoring a data preparation pipeline that uses cuDF and Dask. Which tool would you use to efficiently track the pipeline's performance and resource utilization?

A) DLProf

B) Dask Dashboard

C) NVIDIA Nsight

D) TensorBoard

Show Answer & Explanation

Correct Answer: B

Explanation: The Dask Dashboard provides real-time insights into the performance and resource utilization of Dask tasks, making it the best tool for monitoring a pipeline using cuDF and Dask. DLProf is for model profiling, Nsight for GPU applications, and TensorBoard for TensorFlow models.

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About NCP-ADS Certification

The NCP-ADS certification validates your expertise in data preparation 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.