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

Section	Weight	Objectives
Topic 1: Monitoring and Alerting	10%	- Set up alerts and notifications - Track data lineage and metrics - Monitor pipeline performance and health
Topic 2: Ensuring Data Security and Compliance	10%	- Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit
Topic 3: Debugging and Deploying	10%	- Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs
Topic 4: Data Governance	7%	- Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards
Topic 5: Developing Code for Data Processing using Python and SQL	22%	- Write efficient and maintainable code - Implement complex data processing logic - Use Databricks-specific libraries and APIs
Topic 6: Data Transformation, Cleansing, and Quality	10%	- Implement schema evolution and management - Apply data cleansing and validation rules - Enforce data quality standards
Topic 7: Data Sharing and Federation	5%	- Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access
Topic 8: Cost & Performance Optimisation	13%	- Improve query and pipeline performance - Apply cost management best practices - Optimize compute and storage resources
Topic 9: Data Ingestion & Acquisition	7%	- Ingest data from diverse sources - Handle incremental and batch data loads - Use Auto Loader and structured streaming
Topic 10: Data Modelling	6%	- Implement dimensional and relational models - Optimize table design and partitioning - Design Medallion Architecture

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q216-Q221):

NEW QUESTION # 216
A task orchestrator has been configured to run two hourly tasks. First, an outside system writes Parquet data to a directory mounted at /mnt/raw_orders/. After this data is written, a Databricks job containing the following code is executed:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order, and that the time field indicates when the record was queued in the source system.
If the upstream system is known to occasionally enqueue duplicate entries for a single order hours apart, which statement is correct?

A. The orders table will contain only the most recent 2 hours of records and no duplicates will be present.
B. Duplicate records enqueued more than 2 hours apart may be retained and the orders table may contain duplicate records with the same customer_id and order_id.
C. Duplicate records arriving more than 2 hours apart will be dropped, but duplicates that arrive in the same batch may both be written to the orders table.
D. The orders table will not contain duplicates, but records arriving more than 2 hours late will be ignored and missing from the table.
E. All records will be held in the state store for 2 hours before being deduplicated and committed to the orders table.

Answer: B

NEW QUESTION # 217
A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?

A. validateSchema = false
B. mergeSchema = true
C. overwriteSchema = true
D. ignoreChanges = false

Answer: B

Explanation:
Setting mergeSchema to true allows Delta Lake to automatically evolve the table schema by incorporating new columns from the source data during writes. This enables append-only pipelines to handle schema changes seamlessly in both batch and streaming workloads without breaking the pipeline.

NEW QUESTION # 218
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
B. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
C. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
D. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
E. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.

Answer: C

NEW QUESTION # 219
A data engineering team has a time-consuming data ingestion job with three data sources. Each notebook takes about one hour to load new data. One day, the job fails because a notebook update introduced a new required configuration parameter. The team must quickly fix the issue and load the latest data from the failing source. Which action should the team take?

A. Share the analysis with the failing notebook owner so that they can fix it quickly.
B. Repair the run with the new parameter.
C. Repair the run with the new parameter, and update the task by adding the missing task parameter.
D. Update the task by adding the missing task parameter, and manually run the job.

Answer: C

Explanation:
The repair run capability in Databricks Jobs allows re-execution of failed tasks without re-running successful ones. When a parameterized job fails due to missing or incorrect task configuration, engineers can perform a repair run to fix inputs or parameters and resume from the failed state.
This approach saves time, reduces cost, and ensures workflow continuity by avoiding unnecessary recomputation. Additionally, updating the task definition with the missing parameter prevents future runs from failing.
Running the job manually (B) loses run context; (C) alone does not prevent recurrence; (D) delays resolution. Thus, A follows the correct operational and recovery practice.

NEW QUESTION # 220
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
B. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
C. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
D. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
E. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.

Answer: C

Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.

NEW QUESTION # 221
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