The answer is to use a Glue crawler to populate the Data Catalog and then use dynamic frame from_catalog with partition predicates. This approach eliminates the hardcoded path by having the crawler automatically discover and register all existing partitions in the Data Catalog, so when the Glue ETL job reads via from_catalog, it only processes partitions that actually exist—preventing the “Path does not exist” error for missing days like February 30. On the AWS Certified Data Engineer Associate DEA-C01 exam, this scenario tests your understanding of dynamic partition discovery versus static path references, a common trap where candidates try try-catch blocks or retries instead of fixing the root cause. The key insight is that Glue’s Data Catalog acts as a live index of your S3 partitions, and from_catalog with partition predicates lets you query only valid partitions. Memory tip: “Catalog first, path last”—always let the crawler build the partition map before your job reads.
DEA-C01 Data Ingestion and Transformation 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. 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 retail company uses AWS Glue ETL jobs to process sales data from an S3 data lake. The source data is partitioned by year/month/day in CSV format. The Glue job reads the latest day's data, performs transformations (e.g., cleaning, aggregating), and writes the results to a separate S3 bucket. The job runs on a schedule every day at 2 AM. Recently, the job has been failing intermittently with the error 'AnalysisException: Path does not exist: s3://source-bucket/year=2024/month=02/day=30/'. The engineer verifies that the folder 'day=30' does not exist because February has only 28 days in 2024. The job is reading data from a hardcoded path. The company expects the job to handle variable days per month automatically. What should the engineer do to fix the issue?
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 crawler to populate the Data Catalog and use dynamic frame from_catalog with partition predicates
Option B is correct because using a Glue crawler to update the partition metadata and then using dynamic frame with from_catalog allows Glue to automatically discover all existing partitions. This eliminates the need for hardcoded paths. Option A (try-catch) is a workaround but not a proper solution. Option C (increase retries) does not fix the root cause. Option D (use Spark SQL with manual partition pruning) still requires knowing which partitions exist.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Modify the script to use Spark SQL with manual partition pruning based on current date
Why it's wrong here
Manual pruning still requires handling missing partitions; dynamic frame is more robust.
✗
Add a try-catch block in the script to skip missing partitions
Why it's wrong here
This is a workaround; the job should not attempt to read non-existent partitions.
✗
Increase the job's retry count and set a timeout
Why it's wrong here
Retries will fail again on the same missing partition.
✓
Use a Glue crawler to populate the Data Catalog and use dynamic frame from_catalog with partition predicates
Why this is correct
The crawler discovers existing partitions, and dynamic frame reads only available partitions.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use a Glue crawler to populate the Data Catalog and use dynamic frame from_catalog with partition predicates — Option B is correct because using a Glue crawler to update the partition metadata and then using dynamic frame with from_catalog allows Glue to automatically discover all existing partitions. This eliminates the need for hardcoded paths. Option A (try-catch) is a workaround but not a proper solution. Option C (increase retries) does not fix the root cause. Option D (use Spark SQL with manual partition pruning) still requires knowing which partitions exist.
What should I do if I get this DEA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DEA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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