Databricks Certified-Data-Engineer-Professional real exam prep : Databricks Certified Data Engineer Professional

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

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

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 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.
B. 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.
C. 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.
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.

Question 2

An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
B. Deduplicate records in each batch by pk_id and overwrite the target table.
C. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
D. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.

Question 3

The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over
20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
B. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
E. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.

Question 4

A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.
B. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
C. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
D. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.

Question 5

A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
C. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
D. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Solutions: