Quick Answer
The answer is Amazon SageMaker Data Wrangler and AWS Glue. Both services are correct because they directly address the need to clean, transform, and prepare raw data for machine learning, which is a critical step before any model training can begin. SageMaker Data Wrangler provides a visual interface for data preparation, allowing you to quickly analyze and transform data without writing code, while AWS Glue is a fully managed ETL service that handles large-scale data preprocessing using built-in transforms for structured and semi-structured data. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of which services are purpose-built for data preprocessing rather than just storage or querying. A common trap is confusing Amazon Athena or Amazon Redshift, which are analytics and query engines, with preprocessing tools. Remember the memory tip: if you need to wrangle or glue your data before training, you are looking at Data Wrangler and Glue.
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which TWO services can be used to preprocess data for machine learning in AWS? (Choose two.)
AWS Glue
Glue provides ETL capabilities suitable for preprocessing.
Amazon Athena
Why wrong: Athena is for querying data, not preprocessing.
Amazon SageMaker Data Wrangler
Data Wrangler is specifically for visual data preparation.
Amazon Redshift
Why wrong: Redshift is a data warehouse.
AWS Lambda
Why wrong: Lambda can be used but is not a primary preprocessing service.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Glue
AWS Glue is a fully managed ETL service that can be used to preprocess data for machine learning by cleaning, transforming, and enriching raw data before feeding it into ML models. It provides built-in transforms and can handle both structured and semi-structured data, making it suitable for preparing large datasets for training.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
AWS Glue
Why this is correct
Glue provides ETL capabilities suitable for preprocessing.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Athena
Why it's wrong here
Athena is for querying data, not preprocessing.
Amazon SageMaker Data Wrangler
Why this is correct
Data Wrangler is specifically for visual data preparation.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Redshift
Why it's wrong here
Redshift is a data warehouse.
AWS Lambda
Why it's wrong here
Lambda can be used but is not a primary preprocessing service.
Common exam traps
Cisco often tests the distinction between data querying services (like Athena) and data preprocessing services, leading candidates to mistakenly choose Athena because it can 'process' data via SQL, but it lacks the ML-specific transformation capabilities required for preprocessing.
Detailed technical explanation
AWS Glue uses Apache Spark under the hood to execute distributed ETL jobs, allowing it to handle terabytes of data efficiently. SageMaker Data Wrangler provides a visual interface to perform over 300 built-in transforms, such as handling missing values, one-hot encoding, and scaling, and can export the processed data directly to SageMaker Feature Store or S3 for model training. In real-world scenarios, combining Glue for large-scale batch preprocessing and Data Wrangler for interactive feature engineering is a common pattern.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Glue — AWS Glue is a fully managed ETL service that can be used to preprocess data for machine learning by cleaning, transforming, and enriching raw data before feeding it into ML models. It provides built-in transforms and can handle both structured and semi-structured data, making it suitable for preparing large datasets for training.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 25, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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