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Question 567 of 1,786

Data Operations and Support →hardMultiple ChoiceObjective-mapped

Quick Answer

The correct approach is to use a Glue DynamicFrame and apply the `resolveChoice` method to handle the schema drift. This method directly addresses the intermittent `INSERT has more expressions than target columns` error by allowing you to specify how to manage columns that appear inconsistently across records—for example, by casting them to a common type, dropping them, or making them null—without requiring manual schema updates or fragile conditional logic. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of DynamicFrame’s schema flexibility versus the rigid schema of DataFrames, and it often appears as a trap where candidates mistakenly choose to alter the Redshift table schema or add conditional checks in the ETL script. Remember: when source columns come and go, `resolveChoice` is the pro—it lets you choose how to handle the drift so your job doesn’t sink.

DEA-C01 Data Operations and Support Practice Question

This DEA-C01 practice question tests your understanding of data operations and support. 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 process data from Amazon S3 and write results to Amazon Redshift. The Glue job uses the JDBC connection to Redshift. Recently, the job has been failing intermittently with the error: 'java.sql.SQLException: [Amazon](500310) Invalid operation: INSERT has more expressions than target columns;' The Glue job writes to a staging table in Redshift before performing a merge into the final table. The staging table schema matches the source data. The error occurs only on some days and affects different columns each time. The data engineer suspects that the source data occasionally contains extra columns due to a schema drift in the upstream data producer. Which approach should the data engineer take to handle this issue robustly?

Question 1hardmultiple choice

Full question →

A
Skip any records that have extra columns by adding a conditional check in the Glue script.
Why wrong: Skipping records leads to data loss; the job should handle extra columns gracefully.
B
Use a Glue DynamicFrame and apply the resolveChoice method to make the schema consistent.
resolveChoice can handle schema drift by casting or dropping columns, making the job resilient.
C
Manually update the Redshift staging table schema whenever the source data changes.
Why wrong: Manual intervention is not robust and does not scale; the job should handle schema drift automatically.
D
Use a Glue DynamicFrame and apply the dropFields method to remove extra columns before writing.
Why wrong: dropFields requires knowing which columns to drop; it does not handle dynamic drift automatically.

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

✓

Use a Glue DynamicFrame and apply the resolveChoice method to make the schema consistent.

Option B is correct because Glue DynamicFrames can automatically handle schema drift using the `resolveChoice` method, which allows you to specify how to handle columns that appear inconsistently across records (e.g., making them null, casting to a common type, or dropping them). This directly addresses the intermittent error caused by extra columns in the source data without requiring manual schema updates or fragile conditional logic.

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.

✗
Skip any records that have extra columns by adding a conditional check in the Glue script.
Why it's wrong here
Skipping records leads to data loss; the job should handle extra columns gracefully.
✓
Use a Glue DynamicFrame and apply the resolveChoice method to make the schema consistent.
Why this is correct
resolveChoice can handle schema drift by casting or dropping columns, making the job resilient.
Related concept
Read the scenario before looking for a memorised answer.
✗
Manually update the Redshift staging table schema whenever the source data changes.
Why it's wrong here
Manual intervention is not robust and does not scale; the job should handle schema drift automatically.
✗
Use a Glue DynamicFrame and apply the dropFields method to remove extra columns before writing.
Why it's wrong here
dropFields requires knowing which columns to drop; it does not handle dynamic drift automatically.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may confuse `dropFields` (which removes specific columns statically) with `resolveChoice` (which handles dynamic schema drift), leading them to choose Option D even though it cannot adapt to varying extra columns across different days.

Detailed technical explanation

How to think about this question

The `resolveChoice` method in AWS Glue DynamicFrames can be configured with options like `make_cols` (to add missing columns as nulls) or `project` (to keep only common columns), which is ideal for handling schema drift in semi-structured data like JSON or Parquet. Under the hood, Glue reads the source data into a DynamicFrame, which tracks schema variations per record, and then applies the chosen resolution before writing to the target. In a real-world scenario, this is critical when upstream producers add optional fields (e.g., new metadata tags) without notice, ensuring the ETL job remains resilient without manual intervention.

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.

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FAQ

Questions learners often ask

What does this DEA-C01 question test?

Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use a Glue DynamicFrame and apply the resolveChoice method to make the schema consistent. — Option B is correct because Glue DynamicFrames can automatically handle schema drift using the `resolveChoice` method, which allows you to specify how to handle columns that appear inconsistently across records (e.g., making them null, casting to a common type, or dropping them). This directly addresses the intermittent error caused by extra columns in the source data without requiring manual schema updates or fragile conditional logic.

What should I do if I get this DEA-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.

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This DEA-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 DEA-C01 exam.