MLA-C02 practice question: Data quality, labeling and cleaning
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 dental chain loads visit records from 80 clinics into a data lake every night. A churn-prediction model is retrained from this table each week, and bad records have recently caused the model to train on missing patient IDs and duplicate visits.
The data engineers want rule checks that run inside the nightly AWS Glue ETL job, that stop the load and alert the team when a rule fails, and that use managed rules rather than custom code.
Which approach meets these requirements?
Choose one answer.
AA. Define a Glue Data Quality ruleset in DQDL for completeness and uniqueness, and evaluate it in the ETL job, set to fail the job on rule failure.
BB. Run a weekly SageMaker Processing job that loads the table into pandas and checks for missing values before retraining.
CC. Create a CloudWatch alarm on the number of rows written each night, and page the on-call team when the count changes sharply from the night before.
DD. Enable Amazon Macie on the data lake bucket so that it flags any records with missing patient IDs as sensitive findings.
Show the answer and explanation
Answer: A
A. Right: Correct. Glue Data Quality rulesets written in DQDL run inside the Glue ETL job, and the job can be set to fail without loading the target when a rule fails. The team uses managed rules with no custom code. Learn more: AWS Glue Data Quality (opens in a new tab)
B. Wrong: This checks data after the nightly load and only once a week, so bad data reaches the table first. It also requires custom code, which the stem rules out.
C. Wrong: A row count can change for many good reasons and does not detect missing patient IDs or duplicates. The check is too coarse for the stated problems.
D. Wrong: Macie looks for sensitive data types such as personal identifiers and does not check completeness or uniqueness of a table. It is the wrong tool for this data quality problem.
Explanation
Glue Data Quality rulesets written in DQDL can check completeness and uniqueness inside the ETL job, and a failed rule can stop the load before bad records reach the table. Weekly checks, row counts and Macie findings each miss either the timing or the kind of problem.
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