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

Databricks Certified Data Engineer Professional Sample Questions:

1. A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A) Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
B) Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
C) Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
D) Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
E) The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.

2. A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?

A) Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
B) Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
C) From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.
D) Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.

3. A data engineer needs to design an efficient pipeline that automatically processes new CSV files as they arrive in S3 storage. Which Databricks feature should the data engineer use to meet these requirements?

A) Streaming from cloud storage using standard Spark readStream with format ("csv") and format ("json")
B) COPY INTO SQL command with parameters to track processed files
C) Auto Loader with schema inference and evolution enabled
D) Traditional batch processing with scheduled Databricks Jobs

4. 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, after which logs are archived
C) It is retained for 60 days, during which you can export notebook run results to HTML
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

5. A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A) The Hive metastore is scanned for min and max statistics for the latitude column
B) The Delta log is scanned for min and max statistics for the latitude column
C) The Parquet file footers are scanned for min and max statistics for the latitude column
D) All records are cached to attached storage and then the filter is applied
E) All records are cached to an operational database and then the filter is applied

Solutions: