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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer manages a production Lakeflow Declarative Pipeline that processes customer transaction data. The pipeline includes several data quality expectations such as transaction_amount > 0 and customer_id IS NOT NULL. These expectations are defined using the EXPECT clause in SQL.
The engineer aims to monitor the pipeline's data quality by analyzing the number of records that passed or failed each expectation during the latest pipeline update. The Lakeflow Declarative Pipelines event logs are stored in a Delta table named event_log_table.
For the most recent pipeline update, determine a programmatically appropriate approach to extract information like the name of each expectation, associated dataset, count of records that passed the expectation, and count of records that failed the expectation.
Which method retrieves the desired data quality metrics from the Lakeflow Declarative Pipelines event log?

A) Use the Lakeflow Declarative Pipelines UI to navigate to the specific pipeline, select the dataset, and view the Data Quality tab to manually retrieve the expectation metrics.
B) Access the event_log_table, filter for events where event_type = 'flow_progress', and parse details.flow_progress.data_quality.expectations field to extract the required metrics.
C) Access the event_log_table, filter for events where event_type = 'expectation_result', and extract the expectation metrics from the details field.
D) Query the event_log_table for events with event_type = 'data_quality' and directly select the passed_records and failed_records fields.

2. A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A) Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
B) Reference each expectation with @dlt.expect decorators in the table declaration.
C) Invoke an external API to validate records against the metadata rules.
D) Use a SQL CONSTRAINT block referencing the JSON file path.

3. A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

A) The total count of rows will be returned from cached results unless REFRESH is run
B) The total count of rows is calculated by scanning all data files
C) The total count of records is calculated from the Hive metastore
D) The total count of records is calculated from the parquet file metadata
E) The total count of records is calculated from the Delta transaction logs

4. A data engineer is creating a daily reporting job. There are two reporting notebooks--one for weekdays and one for weekends. An "if/else condition" task is configured as
{{job.start_time.is_weekday}} == true to route the job to either the weekday or weekend notebook tasks. The same job would be used across multiple time zones. Which action should a senior data engineer take upon reviewing the job to merge or reject the pull request?

A) Reject, as they should use {{job.trigger_time.is_weekday}} instead.
B) Reject, as the {{job.start_time.is_weekday}} is not a valid value reference.
C) Merge, as the job configuration looks good.
D) Reject, as the {{job.start_time.is_weekday}} is for the UTC timezone.

5. A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A) databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
B) databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
C) databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
D) databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only

Solutions: