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

Section	Weight	Objectives
Topic 1: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Data governance fundamentals 3. Differences between data lake, data warehouse, and lakehouse
Topic 2: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Monitoring and alerting 3. Jobs and job scheduling
Topic 3: Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Topic 4: Delta Lake	20-25%	- Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions - Delta Lake operations 1. Merge, update, delete operations 2. Delta Live Tables 3. Schema evolution and enforcement
Topic 5: Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Spark APIs in Python 3. Performance optimization techniques

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q182-Q187):

NEW QUESTION # 182
The following table consists of items found in user carts within an e-commerce website.

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The update throws an error because changes to existing columns in the target schema are not supported.
B. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
C. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
D. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.

Answer: B

Explanation:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.
References:
* Databricks documentation on schema evolution in Delta Lake:
https://docs.databricks.com/delta/delta-batch.html#schema-evolution

NEW QUESTION # 183
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?

A. Scala Datasets
B. C++
C. pyspsark.ml.feature
D. Regex
E. Julia

Answer: D

Explanation:
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. References:
* https://docs.databricks.com/notebooks/notebooks-use.html#use-regular-expressions
* https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular-expressions-in-udfs
* https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html
* https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html

NEW QUESTION # 184
A transactions table has been liquid clustered on the columns product_id, user_id, and event_date.
Which operation lacks support for cluster on write?

A. CTAS and RTAS statements
B. spark.write.format('delta').mode('append')
C. spark.writestream.format('delta').mode('append')
D. INSERT INTO operations

Answer: C

Explanation:
Delta Lake'sLiquid Clusteringis an advanced feature that improves query performance by dynamically clustering data without requiring costly compaction steps like traditional Z-ordering.
When performing writes to aLiquid Clusteredtable, some write operations automatically maintain clustering, while othersdo not.
Explanation of Each Option:
* (A) spark.writestream.format('delta').mode('append') (Correct Answer)
* Reason:Streaming writes (writestream) donotsupportLiquid Clusteringbecause streaming data arrives in micro-batches.
* Since Liquid Clustering needs efficient global reorganization of files, streaming append operations don't provide sufficient data volume at a time to be effectively clustered.
* Delta Lake documentation states that Liquid Clustering is only supported for batch writes.
* (B) CTAS and RTAS statements
* Reason:CREATE TABLE AS SELECT (CTAS) and REPLACE TABLE AS SELECT (RTAS) are batch operationsand can enforce Liquid Clustering.
* These operations create or replace a table based on a query result, and since they are batch-based, Liquid Clustering applies.
* (C) INSERT INTO operations
* Reason:INSERT INTOis supportedfor Liquid Clustering because it is a batch operation.
* While it may not be as efficient as MERGE or COPY INTO, clustering is applied upon execution.
* (D) spark.write.format('delta').mode('append')
* Reason:Batch append operationsare supportedfor Liquid Clustering.
* Unlike streaming append, batch writes allow the optimizer to re-cluster data efficiently.
Conclusion:
Sincestreaming append operations do not support Liquid Clustering, option(A)is the correct answer.
References:
* Liquid Clustering in Delta Lake - Databricks Documentation

NEW QUESTION # 185
A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
D. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
E. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.

Answer: D

NEW QUESTION # 186
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users .

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

A. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
B. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
C. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
D. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
E. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

Answer: E

Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under
"Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 187
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