Databricks Certified Data Engineer Professional : Databricks-Certified-Data-Engineer-Professional exam dump

A data engineer is troubleshooting a slow-running Delta Lake query on Databricks SQL involves complex joins and large datasets. They need to identify whether the root cause is related to poor data skipping, inefficient join strategies, or excessive data shuffling. Which approach should identify the specific bottlenecks using native Databricks tools?

• A. Use the LIMIT clause to run a subset of the query and compare execution times with the full dataset.
• B. Enable the EXPLAIN command to review the parsed logical plan and manually estimate shuffle sizes.
• C. Analyze the Top Operators panel in the Query Profile to identify high-cost operations like BroadcastNestedLoopJoin
• D. Check the query's execution time in the Jobs UI and correlate it with cluster resource utilization metrics.

A data engineer is using Lakeflow Declarative Pipeline to propagate row deletions from a source bronze table (user_bronze) to a target silver table (user_silver). The engineer wants deletions in user_bronze to automatically delete corresponding rows in user_silver during pipeline execution.
Which configuration ensures deletions in the bronze table are propagated to the silver table?

• A. Configure VACUUM on user_bronze to delete files, then rebuild user_silver from scratch.
• B. Use apply_changes without CDF and filter rows where _soft_deleted is true.
• C. Enable CDF on user_silver, read its transaction log, and use MERGE to sync deletions.
• D. Enable Change Data Feed (CDF) on user_bronze, read its CDF stream, and use apply_changes() with apply_as_deletes=True for user_silver.

What statement is true regarding the retention of job run history?

• A. It is retained for 30 days, during which time you can deliver job run logs to DBFS or S3
• B. It is retained for 60 days, during which you can export notebook run results to HTML
• C. It is retained for 60 days, after which logs are archived
• D. It is retained until you export or delete job run logs
• E. It is retained for 90 days or until the run-id is re-used through custom run configuration

A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

• A. delayWrite("event_time", "10 minutes")
• B. await("event_time + `10 minutes'")
• C. withWatermark("event_time", "10 minutes")
• D. slidingWindow("event_time", "10 minutes")
• E. awaitArrival("event_time", "10 minutes")

A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

• A. At some point, the upstream data provider switched everything to multi-line JSON.
• B. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
• C. The source data is valid JSON but does not conform to the defined schema in some way.
• D. The badRecordsPath location is accumulating many small files.