Implement Quality Assurance DA0-001 Practice Questions (Page 4)

Questions 16–20

1. 16expert · hard

   A data governance team is defining quality metrics for a customer database. They have two candidate metrics: (1) the percentage of records with a valid phone number format, and (2) the percentage of records where the 'state' field matches the 'zip code' field. The team wants to prioritize metrics that measure accuracy rather than just format. Which metric should they prioritize?

   • ANeither metric, because accuracy cannot be measured without a third-party source of truth
   • BMetric 1, because phone number format is the most important customer attribute
   • CMetric 2, because it validates the correctness of the data by checking the relationship between two fields
   • DBoth metrics equally, because they both measure accuracy
   Reveal Solution Discussion Select an answer first
2. 17application · medium

   During a data profiling exercise, an analyst discovers that the 'order_date' field in a sales table contains dates from the future (e.g., next year's dates) and dates from 20 years ago. The analyst needs to document this issue for the data governance team. What is the most appropriate way to document this finding?

   • ADelete the records with future dates immediately to clean the data
   • BSend an informal email to the data entry team mentioning that some dates look wrong
   • CCreate a data quality issue log entry describing the field, the type of issue (invalid date range), the number of affected records, and a sample of the problematic values
   • DIgnore the issue because profiling is only meant to describe data, not to report problems
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   A data analyst is reviewing a customer table to understand the distribution of values in the 'age' column, including the minimum, maximum, and average. Which data profiling technique is being applied?

   • ACross-table profiling
   • BData lineage analysis
   • CColumn profiling
   • DData rule validation
   Reveal Solution Discussion Select an answer first
5. 19application · medium

   A streaming data pipeline ingests sensor readings from IoT devices. The data quality team wants to detect if a sensor starts sending null values or values outside a plausible range (e.g., temperature between -40 and 150 degrees Fahrenheit). Which monitoring tool feature is most appropriate?

   • AA nightly batch job that summarizes the average temperature per sensor
   • BA manual review of the data lake contents every week
   • CReal-time data quality rules that validate each incoming record against null and range checks and flag violations
   • DA monthly report that lists all sensors and their data volumes
   Reveal Solution Discussion Select an answer first
6. 20application · medium

   A financial institution has defined a data quality standard stating that all transaction records must have a non-null transaction ID, a valid ISO currency code, and an amount greater than zero. The QA team needs to validate a new batch of data against these rules before it is loaded into the production system. Which testing method should they use?

   • APerform a visual spot-check of a random sample of 50 records
   • BCompare the new batch's schema to the production table's schema
   • CRun a data profiling summary to view the distinct values in each column
   • DRun a set of validation queries that filter for records violating each rule and report the count of failures
   Reveal Solution Discussion Select an answer first