Question 947 of 986
ETL Pipeline Failure Troubleshooting for CompTIA Data+
Exhibit
2023-03-15 14:32:10 ERROR: Failed to load data from source: Timeout expired. 2023-03-15 14:32:10 WARN: Retry attempt 1/3... 2023-03-15 14:32:15 ERROR: Retry 1 failed: Connection reset. 2023-03-15 14:32:15 WARN: Retry attempt 2/3... 2023-03-15 14:32:20 ERROR: Retry 2 failed: Server not responding. 2023-03-15 14:32:20 WARN: Retry attempt 3/3... 2023-03-15 14:32:25 ERROR: All retries exhausted. Pipeline failed.
Refer to the exhibit. An ETL pipeline failed. What is the most likely root cause?
Quick Answer
The answer is network connectivity issues. When an ETL pipeline fails with a timeout error, it almost always points to a breakdown in communication between the pipeline and its source or target system; the process hangs while waiting for a response, eventually exceeding the timeout threshold and failing the job. On the CompTIA Data+ DA0-001 exam, this scenario tests your ability to distinguish network problems from other common failures like data format mismatches or authentication errors—timeouts are the classic trap for confusing a connectivity issue with a data quality issue. A quick memory tip: think of a timeout as a “no response” signal, not a “bad data” signal.
⚠ Common exam trap
Many candidates confuse a timeout error with a data format mismatch or authentication failure, but the certification tests the understanding that timeouts are uniquely tied to network connectivity issues, not to data content or access permissions.
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
✓
Network connectivity issues
The exhibit shows an ETL pipeline failure with a timeout error, which is a classic symptom of network connectivity issues. When the pipeline cannot reach the source or target system due to a network interruption, the process hangs until a timeout threshold is exceeded, causing the job to fail. This is the most likely root cause because timeouts are directly associated with network problems, not with data format, authentication, or storage issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data format mismatch
Why it's wrong here
Data format errors would be schema-related, e.g., 'cannot parse'.
- ✗
Authentication failure
Why it's wrong here
Auth failures would show 'access denied' or 'invalid credentials'.
- ✗
Insufficient disk space
Why it's wrong here
Disk space issues would show 'disk full' errors, not network timeouts.
- ✓
Network connectivity issues
Why this is correct
Timeouts and connection resets are classic signs of network problems.
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Same concept, more angles
1 more way this is tested on DA0-002
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company has a data warehouse that integrates sales data from multiple sources including online transactions, in-store POS, and third-party marketplaces. The data team recently updated the ETL pipeline to add a new data source: mobile app purchases. After the update, the daily sales report shows a 15% increase in total sales compared to the previous day, which is unexpected because the mobile app is new and only contributed 2% of sales in tests. The report is created by a SQL script that aggregates sales by date and runs every morning. The data team needs to identify the cause of the discrepancy. Which of the following should the team do first?
easy- ✓ A.Verify that the date filter in the SQL script is correct and not including future dates.
- B.Compare the raw transaction counts from each source for that day.
- C.Assume the increase is due to the mobile app and update the forecast.
- D.Check if the ETL pipeline is double-counting transactions from the mobile app source.
Why A: The correct first step because an unexpected 15% increase in total sales, especially when the new mobile app source accounts for only 2% in tests, suggests a data integrity issue rather than a real sales surge. The most common cause is a date filter error in the SQL script, such as including future dates or applying an incorrect date range, which would inflate the daily total. Verifying the date filter is a quick, low-effort check that can confirm the data source before investigating more complex issues like double-counting (Option D). Options B and C are premature: comparing raw transaction counts is a secondary step after confirming the date filter, and assuming the increase is real (C) ignores the most likely data problem.
Last reviewed: Jun 24, 2026
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