DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer is using AWS Glue Studio to build a job that reads from an Amazon S3 bucket containing nested JSON files. The job must flatten the nested structure and write the output to Amazon Redshift. The engineer notices that the job fails with an error indicating that the Redshift table does not exist. The engineer has already created the Redshift cluster and database. What should the engineer do to resolve the error?

⚠ Common exam trap

The trap here is thinking that AWS Glue can create the Redshift table automatically, similar to how it might create a Data Catalog table, when in fact Redshift tables must be pre-created.

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

✓

Create the target table in Amazon Redshift with the appropriate schema before running the job.

When writing to Amazon Redshift from AWS Glue, the target table must already exist. Glue does not automatically create Redshift tables. The engineer should create the table with the correct schema in Redshift, then rerun the job. The other transforms mentioned are for data manipulation within the job and do not address the missing table.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
Create the target table in Amazon Redshift with the appropriate schema before running the job.
Why this is correct
AWS Glue jobs writing to Amazon Redshift require the target table to exist beforehand. The error indicates the table is missing. The engineer must create the table in Redshift with a schema that matches the output of the Glue job, including column names and data types. This is a common prerequisite when using the Redshift connector in Glue Studio.
✗
Add a 'ResolveChoice' transform before the Redshift writer to handle nested types.
Why it's wrong here
The 'ResolveChoice' transform is used to handle ambiguous data types (for example, a column that is sometimes int and sometimes string) by choosing one type. It does not create the target Redshift table. The error is specifically about the table not existing, so resolving choice will not address the missing table.
✗
Use the 'ApplyMapping' transform to map columns to Redshift data types.
Why it's wrong here
The 'ApplyMapping' transform is used to rename columns, change data types, and drop columns in the Glue job's data frame. It does not create or alter tables in Redshift. While mapping is important for ensuring compatibility, it does not resolve the missing table error. The table must exist before the job writes to it.
✗
Configure the Redshift writer to use the 'CREATE TABLE' option and specify the table name.
Why it's wrong here
In AWS Glue Studio, the Redshift writer does not have a 'CREATE TABLE' option. The job expects the target table to exist. While Glue can create tables in the Data Catalog, it does not automatically create tables in Redshift. The engineer must create the table manually or via a separate step.

Visual reference

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

Go deeper

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question