The correct diagnostic step is to check the source database table sizes and row counts over time. This is because an AWS Glue timeout error, which occurs when a job exceeds the default 300-second maximum, is often a symptom of growing data volume rather than a code or configuration flaw. By analyzing data volume trends, you can determine if the source dataset has expanded to the point where the current job resources or timeout setting are insufficient, directly addressing the root cause rather than masking it. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to apply exploratory data analysis (EDA) to operational issues, a common scenario where candidates mistakenly jump to increasing the timeout or changing the job type—both of which are temporary fixes that ignore the underlying data growth. A useful memory tip: "Timeout tells you it's too slow, so check the data's growth—don't just let the clock grow."
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
Exhibit
Refer to the exhibit.
[ERROR] 2023-01-15T10:30:00.000Z 12345678-1234-1234-1234-123456789012
Task timed out after 300.00 seconds
[ERROR] 2023-01-15T10:35:00.000Z 12345678-1234-1234-1234-123456789012
Task timed out after 300.00 seconds
Refer to the exhibit. A data scientist is using AWS Glue ETL jobs to process data from a source database. The job logs show repeated timeout errors. Which EDA step should the scientist perform to diagnose the issue?
Refer to the exhibit.
[ERROR] 2023-01-15T10:30:00.000Z 12345678-1234-1234-1234-123456789012
Task timed out after 300.00 seconds
[ERROR] 2023-01-15T10:35:00.000Z 12345678-1234-1234-1234-123456789012
Task timed out after 300.00 seconds
A
Test network connectivity from the Glue job to the source database using telnet.
Why wrong: Connectivity issues would cause different errors.
B
Check the source database table sizes and row counts over time.
Identifies if data volume growth causes timeouts.
C
Switch the Glue ETL job type from Spark to Python shell to reduce overhead.
Why wrong: Python shell has even smaller timeout limits.
D
Increase the Glue job timeout to 600 seconds and rerun.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check the source database table sizes and row counts over time.
The timeout suggests the job is taking longer than the max 300 seconds. Analyzing source data volume trends helps determine if data size has increased, causing longer processing time. Option A is wrong because changing job type does not address root cause. Option B is wrong because increasing timeout without understanding data growth is a temporary fix. Option D is wrong because the error is about timeout, not connectivity.
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.
✗
Test network connectivity from the Glue job to the source database using telnet.
Why it's wrong here
Connectivity issues would cause different errors.
✓
Check the source database table sizes and row counts over time.
Why this is correct
Identifies if data volume growth causes timeouts.
Related concept
Read the scenario before looking for a memorised answer.
✗
Switch the Glue ETL job type from Spark to Python shell to reduce overhead.
Why it's wrong here
Python shell has even smaller timeout limits.
✗
Increase the Glue job timeout to 600 seconds and rerun.
Why it's wrong here
Workaround, not diagnostic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Check the source database table sizes and row counts over time. — The timeout suggests the job is taking longer than the max 300 seconds. Analyzing source data volume trends helps determine if data size has increased, causing longer processing time. Option A is wrong because changing job type does not address root cause. Option B is wrong because increasing timeout without understanding data growth is a temporary fix. Option D is wrong because the error is about timeout, not connectivity.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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