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

Section	Weight	Objectives
Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Spark APIs in Python 2. Performance optimization techniques 3. Built-in and user-defined functions - Spark DataFrames and Spark SQL 1. Window functions 2. Spark SQL queries and functions 3. DataFrame operations and transformations
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Bronze, silver, gold data layers 3. Differences between data lake, data warehouse, and lakehouse
Delta Lake	20-25%	- Delta Lake fundamentals 1. Time travel and data versioning 2. ACID transactions 3. Optimize and Z-order - Delta Lake operations 1. Delta Live Tables 2. Merge, update, delete operations 3. Schema evolution and enforcement
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Task dependencies and orchestration 3. Monitoring and alerting
Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems - Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A. Shallow clone the table for the analytics team.
B. Create a new table for the analytics team using a CTAS statement.
C. Deep clone the table for the analytics team.
D. Give the analytics team direct access to the production table.

Answer: A

Explanation:
Exact extract: "A shallow clone creates a copy of the metadata that references the source data files; it is fast and inexpensive." Exact extract: "A deep clone copies the data."

NEW QUESTION # 90
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?

A. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
B. No: the change data feed only tracks inserts and updates not deleted records.
C. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
D. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.

Answer: D

Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 91
if you run the command VACUUM transactions retain 0 hours? What is the outcome of this command?

A. Command will be successful, but no data is removed
B. Command will fail, you cannot run the command with retentionDurationcheck enabled
C. Command will fail if you have an active transaction running
D. Command runs successful and compacts all of the data in the table
E. Command will be successful, but historical data will be removed

Answer: B

Explanation:
Explanation
The answer is,
Command will fail, you cannot run the command with retentionDurationcheck enabled.
1.VACUUM [ [db_name.]table_name | path] [RETAIN num HOURS] [DRY RUN]
*Recursively vacuum directories associated with the Delta table and remove data files that are no longer in the latest state of the transaction log for the table and are older than a retention threshold. Default is 7 Days.
*The reason this check is enabled is because, DELTA is trying to prevent unintentional deletion of history, and also one important thing to point out is with 0 hours of retention there is a possibility of data loss(see below kb) Documentation in VACUUM https://docs.delta.io/latest/delta-utility.html
https://kb.databricks.com/delta/data-missing-vacuum-parallel-write.html

NEW QUESTION # 92
A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late- arriving data is common.
Which approach should the data engineer use?

A. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
B. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
C. Use an Auto CDC pipeline with batch tables to simplify late data handling.
D. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.

Answer: B

Explanation:
In Structured Streaming, event-time watermarks control how long the engine waits for late-arriving data before finalizing aggregations. By setting an appropriate watermark, Databricks can handle late data gracefully - incorporating records that arrive within the defined window while discarding excessively delayed events.
This approach ensures accurate aggregations, minimizes state size, and prevents memory leaks.
Manual reprocessing (A) or overwriting entire datasets (B) is inefficient and costly, while Auto CDC (C) is used for change tracking in Delta tables, not for streaming event lateness.
Thus, using watermarking is the recommended and official approach for managing late data in streaming pipelines.

NEW QUESTION # 93
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:

Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
B. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
C. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
D. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
E. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.

Answer: B

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

NEW QUESTION # 94
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