- A
Use the 'mergeSchema' option when reading the DynamicFrame.
Why wrong: Incorrect. `mergeSchema` is only available for Parquet/ORC formats, not CSV. Using it with CSV will have no effect on schema mismatches.
- B
Convert all CSV files to Parquet format using a separate preprocessing job.
Why wrong: Incorrect. Converting to Parquet requires an extra preprocessing job, which is less efficient and adds complexity.
- C
Define a fixed schema in the Glue job using 'apply_mapping' to map columns.
Correct. Defining a fixed schema and using `apply_mapping` to map columns effectively handles inconsistent column counts by ensuring a consistent schema is applied to all files.
- D
Set the job to 'ignore' schema mismatches in the job parameters.
Why wrong: Incorrect. AWS Glue does not provide a parameter to 'ignore' schema mismatches. The job will still fail on schema detection errors.
DEA-C01 Fixed Schema Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: fixed Schema. 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 process CSV files from an S3 bucket. The job fails intermittently with a 'SchemaDetectionError' for files that have inconsistent column counts. What is the most efficient way to handle this?
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
Define a fixed schema in the Glue job using 'apply_mapping' to map columns.
Defining a fixed schema using the `schema` parameter in the DynamicFrame reader forces Glue to apply that schema to all CSV files. With `apply_mapping`, you can map the actual columns present to the fixed schema, handling inconsistent column counts by ignoring extra columns and filling missing columns with nulls. This avoids schema detection errors without extra preprocessing.
Key principle: Fixed Schema
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the 'mergeSchema' option when reading the DynamicFrame.
Why it's wrong here
Incorrect. `mergeSchema` is only available for Parquet/ORC formats, not CSV. Using it with CSV will have no effect on schema mismatches.
- ✗
Convert all CSV files to Parquet format using a separate preprocessing job.
Why it's wrong here
Incorrect. Converting to Parquet requires an extra preprocessing job, which is less efficient and adds complexity.
- ✓
Define a fixed schema in the Glue job using 'apply_mapping' to map columns.
Why this is correct
Correct. Defining a fixed schema and using `apply_mapping` to map columns effectively handles inconsistent column counts by ensuring a consistent schema is applied to all files.
Related concept
Fixed Schema
- ✗
Set the job to 'ignore' schema mismatches in the job parameters.
Why it's wrong here
Incorrect. AWS Glue does not provide a parameter to 'ignore' schema mismatches. The job will still fail on schema detection errors.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is assuming `mergeSchema` works for CSV files. In AWS Glue, `mergeSchema` is only supported for Parquet/ORC formats. For CSV, you must define a fixed schema and use `apply_mapping` to handle inconsistencies.
Detailed technical explanation
How to think about this question
Under the hood, when 'mergeSchema' is set to true, Glue's Spark-based execution engine performs a two-pass approach: it first samples files to collect all unique schemas, then merges them using Spark's schema merging logic (similar to Parquet's mergeSchema), adding columns with null values for files that lack them. This is particularly useful in streaming or incremental data ingestion where schema drift is common, but it incurs a slight performance cost due to the additional sampling and schema reconciliation step.
KKey Concepts to Remember
- Fixed Schema
- apply_mapping
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
Fixed Schema
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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
Got this wrong? Here's your next step.
Review fixed Schema, then practise related DEA-C01 questions on the same topic to reinforce the concept.
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Data Ingestion and Transformation — study guide chapter
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Fixed Schema.
What is the correct answer to this question?
The correct answer is: Define a fixed schema in the Glue job using 'apply_mapping' to map columns. — Defining a fixed schema using the `schema` parameter in the DynamicFrame reader forces Glue to apply that schema to all CSV files. With `apply_mapping`, you can map the actual columns present to the fixed schema, handling inconsistent column counts by ignoring extra columns and filling missing columns with nulls. This avoids schema detection errors without extra preprocessing.
What should I do if I get this DEA-C01 question wrong?
Review fixed Schema, then practise related DEA-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Fixed Schema
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Last reviewed: Jun 11, 2026
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