MLA-C02 practice question: Transforming data with Glue, EMR and Data Wrangler (1 of 2)

Data Preparation for ML and AI A free question from our original MLA-C02 practice exam. Try it, then open the answer to see why each option is right or wrong.

A regional bank loads daily transaction files into Amazon S3, partitioned by business date. A nightly AWS Glue Spark job reads the raw transactions, builds the training table, and writes it to the curated bucket.

The job currently reprocesses all files since launch, which takes most of the night and grows each month. The team must process only the files and partitions that are new since the last successful run, without changing the source files or the bank's daily upload process.

Which TWO changes meet these requirements? (Choose two.)

Choose two answers.

A. Set the job to overwrite the entire curated table on every run, so the output is always rebuilt from the full history.
B. Enable job bookmarks on the Glue job so that Glue tracks which input data it has already processed in earlier runs.
C. Add more DPUs or workers to the job so that the full history finishes faster each night.
D. Use a push-down predicate that limits the read to the business-date partitions that are newer than the last successful run.
E. Run an AWS Glue Data Quality ruleset on the raw transactions before the job starts so that bad rows are removed from the input.

Show the answer and explanation

Answers: B, D

A. Wrong: A full rebuild is the cause of the slowdown, not the fix. The job would still read and write all the history each night.
B. Right: Correct. Job bookmarks record processed S3 objects between runs, so each run reads only new input. This needs no change to the source files or the upload process. Learn more: Tracking processed data with job bookmarks (opens in a new tab)
C. Wrong: More workers shorten the run but still reprocess everything, so the job keeps growing with each month of data. The requirement asks for incremental processing.
D. Right: Correct. A push-down predicate lets Glue read only the partitions that match a filter, so the job skips older dates. Combined with bookmarks, the job reads only new partitions and files. Learn more: Managing partitions for ETL output in AWS Glue (opens in a new tab)
E. Wrong: Data quality rules check or filter rows, but they do not limit which input the job reads. The job would still process all files on every run.

Explanation

Job bookmarks and push-down predicates both limit a Glue job to new input: bookmarks track what earlier runs processed, and predicates filter the partitions read. Neither changes the source data or the daily upload. Overwriting the table or adding workers still reprocesses the whole history.

MLA-C02 practice question: Transforming data with Glue, EMR and Data Wrangler (1 of 2)

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