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Question 413 of 1,755

Machine Learning Implementation and Operations →easyMultiple ChoiceObjective-mapped

Why Your AWS Glue ETL Job Produces Empty Output: Overly Restrictive Filter

This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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.

A team uses AWS Glue ETL jobs to preprocess data for SageMaker training. The job runs successfully but the output data is empty. What is the most likely cause?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

A
There is a data type mismatch between source and target
Why wrong: This would cause errors, not empty output.
B
The source data is partitioned and only a subset of partitions is read
Why wrong: Partition pruning reads only relevant partitions, but data should still exist.
C
The filter transformation condition is too restrictive, removing all rows
Filtering all rows results in empty output.
D
The Glue job runs out of memory and fails silently
Why wrong: Out of memory causes job failure, not empty output.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

The filter transformation condition is too restrictive, removing all rows

Option C is correct because a filter transformation in AWS Glue ETL jobs can remove all rows if the condition is too restrictive, resulting in an empty output dataset. This is a common logic error where the filter predicate (e.g., `df.filter("value > 100")`) matches no records, causing the DynamicFrame to be empty after transformation. The job succeeds because no runtime error occurs, but the output is empty.

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.

✗
There is a data type mismatch between source and target
Why it's wrong here
This would cause errors, not empty output.
✗
The source data is partitioned and only a subset of partitions is read
Why it's wrong here
Partition pruning reads only relevant partitions, but data should still exist.
✓
The filter transformation condition is too restrictive, removing all rows
Why this is correct
Filtering all rows results in empty output.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The Glue job runs out of memory and fails silently
Why it's wrong here
Out of memory causes job failure, not empty output.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume empty output must be caused by a failure or resource issue (like memory or partitioning), rather than a logical error in the transformation logic that silently removes all data.

Trap categories for this question

Command / output trap
This would cause errors, not empty output.

Detailed technical explanation

How to think about this question

In AWS Glue, filter transformations are applied using Spark SQL or DynamicFrame methods like `filter()` or `drop_fields()`. If the filter condition evaluates to false for every row (e.g., `df.filter("age > 200")` on a dataset where max age is 150), the resulting DynamicFrame has zero records. This is a silent logical error because Glue does not validate that the filter produces non-empty output; it only checks syntax. In real-world pipelines, such issues often arise from incorrect date comparisons or mismatched column names in the filter predicate.

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 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

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.

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FAQ

Questions learners often ask

What does this MLS-C01 question test?

Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The filter transformation condition is too restrictive, removing all rows — Option C is correct because a filter transformation in AWS Glue ETL jobs can remove all rows if the condition is too restrictive, resulting in an empty output dataset. This is a common logic error where the filter predicate (e.g., `df.filter("value > 100")`) matches no records, causing the DynamicFrame to be empty after transformation. The job succeeds because no runtime error occurs, but the output is empty.

What should I do if I get this MLS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLS-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 MLS-C01 exam.