DEA-C01 Data Operations and Support Practice Question
A data engineer maintains an AWS Glue Data Catalog table for an S3-based dataset. After new files were added, queries in Amazon Athena fail with the error 'HIVE_BAD_DATA: Error parsing field value for field 3: For input string: "N/A"'. The column is defined as bigint in the Data Catalog but contains the string 'N/A' in some records. Which action should the data engineer take to allow Athena to query the data without changing the underlying files?
⚠ Common exam trap
The trap here is assuming that partition-level operations like MSCK REPAIR TABLE or partition projection can resolve column-level data type errors.
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
✓
Alter the table to set the column type to string in the AWS Glue Data Catalog.
The error is caused by a mismatch between the declared bigint type and actual string values in the files. The most direct fix that avoids rewriting data is to change the column type to string in the AWS Glue Data Catalog. Athena will then read the field as text, and the query will no longer attempt numeric conversion. Partition repair, projection, and SerDe tweaks do not address the type conversion failure.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Add a partition projection configuration for the table.
Why it's wrong here
Partition projection speeds up query planning by avoiding metadata lookups for partitions, but it does not influence column-level data type validation. The HIVE_BAD_DATA error arises because Athena cannot cast the string 'N/A' to bigint. Projection would not change the underlying schema or the values being read, so the query would still fail with the same parsing error.
- ✗
Run MSCK REPAIR TABLE on the table to refresh partitions.
Why it's wrong here
MSCK REPAIR TABLE only synchronizes partition metadata with the S3 folder structure. It does not alter column data types or handle invalid values such as 'N/A' within data files. Because the failure is a type-conversion error during query execution, repairing partitions will not change how Athena parses the bigint column, so the same HIVE_BAD_DATA error will recur.
- ✗
Create a new table with the column as bigint and use a SerDe that skips malformed rows.
Why it's wrong here
A SerDe that skips malformed rows is not available in Athena for this purpose; the OpenCSVSerDe and similar SerDes do not provide a toggle to drop rows based on type mismatch. The error occurs at the columnar read level, not in row parsing. Creating another table with the same bigint type will reproduce the HIVE_BAD_DATA failure, so this does not solve the problem.
- ✓
Alter the table to set the column type to string in the AWS Glue Data Catalog.
Why this is correct
Changing the column type to string prevents Athena from attempting numeric conversion on values like 'N/A', allowing the query to succeed without modifying the files. The data engineer can update the table schema through the AWS Glue Data Catalog, and Athena will then treat the field as text. This is the least invasive way to handle mixed or invalid data when the source files cannot be rewritten.
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 |
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Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.