The correct choice is to modify the Glue job to use a dynamic frame and enable schema updates with an `applyMapping` that includes new columns. This is because AWS Glue DynamicFrames natively handle schema evolution by allowing you to apply a mapping that can include new fields, using `resolveChoice` to define how to treat unexpected columns—such as casting them to a specific type or keeping them as a struct—thereby preventing job failures when the source schema changes. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to build resilient ETL pipelines for machine learning data preparation, where source schemas in S3 frequently evolve. A common trap is assuming a fixed schema in the job script is sufficient, but DynamicFrames are designed specifically to adapt to changing schemas without manual script rewrites. Memory tip: think “Dynamic = Flexible” — DynamicFrames flexibly absorb new columns, while static frames break.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
A company uses AWS Glue to run ETL jobs that prepare data for machine learning. The source data in S3 has a schema that evolves over time (new columns are added occasionally). The Glue job schema is defined as a fixed schema in the job script. After an update to the source data, the Glue job fails with an error about mismatched schemas. How should the data engineer modify the data preparation process to handle schema evolution?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Modify the Glue job to use a dynamic frame and enable schema updates with a 'applyMapping' that includes new columns
Option A is correct because AWS Glue DynamicFrames natively handle schema evolution by allowing you to apply a mapping that can include new columns. By using `applyMapping` with `resolveChoice`, you can define how to handle new fields (e.g., cast to a type or keep as a struct), preventing job failures when the source schema changes. This avoids the rigidity of a fixed schema in the job script.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✓
Modify the Glue job to use a dynamic frame and enable schema updates with a 'applyMapping' that includes new columns
Why this is correct
Dynamic frames with schema detection can adapt to schema changes.
Related concept
Read the scenario before looking for a memorised answer.
✗
Run a Glue crawler before each job to update the Data Catalog, but keep the fixed schema in the job
Why it's wrong here
The job still uses its fixed schema, causing failure.
✗
Store the schema definition in a separate file in S3 and read it at runtime
Why it's wrong here
This does not automatically adapt the job to new columns.
✗
Manually update the Glue job script each time the schema changes
Why it's wrong here
This is error-prone and not scalable.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume updating the Data Catalog via a crawler is sufficient, but they miss that the job script's fixed schema must also be updated or made dynamic to avoid mismatches.
Detailed technical explanation
How to think about this question
DynamicFrames in AWS Glue use the `resolveChoice` method to handle schema conflicts, such as when a column appears as a string in one partition and an int in another. Under the hood, Glue can project the schema dynamically by reading the data's structure at runtime, allowing `applyMapping` to map new columns to a target type or drop them. In a real-world scenario, this is critical for streaming or incremental data where new columns are added frequently without prior notice.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Modify the Glue job to use a dynamic frame and enable schema updates with a 'applyMapping' that includes new columns — Option A is correct because AWS Glue DynamicFrames natively handle schema evolution by allowing you to apply a mapping that can include new columns. By using `applyMapping` with `resolveChoice`, you can define how to handle new fields (e.g., cast to a type or keep as a struct), preventing job failures when the source schema changes. This avoids the rigidity of a fixed schema in the job script.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This MLA-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 MLA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.