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

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

Databricks Certified Data Engineer Professional Sample Questions:

1. A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A) Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
B) The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
C) Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
D) No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
E) The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

2. Which method can be used to determine the total wall-clock time it took to execute a query?

A) Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
B) Open the Query Profiler associated with that query and use the Aggregated task time metric.
C) In the Spark UI, take the job duration of the longest-running job associated with that query.
D) In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.

3. 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-from TODAY_MIDNIGHT_EPOCH_MS -- active-only
D) databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only

4. A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

A) Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
B) Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
C) Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.
D) Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.

5. A data engineer is implementing liquid clustering on a Delta Lale table and needs to understand how it affects data management operations. The table will be updated frequently with new data.
The table is an external table and not managed by Unity Catalog. How does liquid clustering in Delta Lake handle new data that is inserted after the initial table creation?

A) New data remains unclustered until the next OPTIMIZE operation.
B) New data is rejected if it doesn't match the clustering pattern.
C) New data is automatically clustered during write operations.
D) New data is written to a staging area and clustered during scheduled maintenance.

Solutions: