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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

A. Increase the shuffle partitions to account for additional aggregates
B. Specify a new checkpointlocation
C. Run REFRESH TABLE delta, /item_agg'
D. Remove .option (mergeSchema', true') from the streaming write

Answer: B

Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
Reference:
Databricks documentation on Structured Streaming: https://docs.databricks.com/spark/latest/structured-streaming/index.html Databricks documentation on streaming checkpoints: https://docs.databricks.com/spark/latest/structured-streaming/production.html#checkpointing

NEW QUESTION # 36
A new data engineer new.engineer@company.com has been assigned to an ELT project. The new data
engineer will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the table to the new data engineer?

A. 1. GRANT ALL PRIVILEGES ON TABLE new.engineer@company.com TO sales;
B. 1. GRANT SELECT ON TABLE sales TO new.engineer@company.com;
C. 1. GRANT USAGE ON TABLE sales TO new.engineer@company.com;
D. 1. GRANT ALL PRIVILEGES ON TABLE sales TO new.engineer@company.com;
E. 1. GRANT SELECT CREATE MODIFY ON TABLE sales TO new.engineer@company.com;

Answer: D

NEW QUESTION # 37
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. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, 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. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), 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.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

Answer: B

NEW QUESTION # 38
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
B. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
C. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
D. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

Answer: B

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.
Reference:
Databricks Documentation on Delta Lake: Delta Lake Guide
Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

NEW QUESTION # 39
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

A. SHOW TABLES dev
B. DESCRIBE EXTENDED dev.pii test
C. DESCRIBE HISTORY dev.pii test
D. DESCRIBE DETAIL dev.pii test
E. SHOW TBLPROPERTIES dev.pii test

Answer: B

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified Reference: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.

NEW QUESTION # 40
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