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Databricks, a unified analytics platform provider that helps organizations process and analyze large data sets, offers a certification exam for data engineers called the Databricks Certified Professional Data Engineer. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the skills and knowledge of data engineers in building and managing data pipelines, ETL processes, and data architectures within the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is intended to validate the expertise of data engineers in implementing and managing data projects, and to demonstrate their competency in using the Databricks platform.

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Databricks Certified Professional Data Engineer certification exam is a challenging exam that requires candidates to have a deep understanding of Databricks technologies and data engineering concepts. Candidates must have experience working with Apache Spark, Delta Lake, SQL, and Python. They must also have experience working with cloud-based data platforms such as AWS, Azure, or Google Cloud Platform.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q200-Q205):

NEW QUESTION # 200
A data team is implementing an append-only Delta Lake pipeline that needs to handle both batch and streaming data. They want to ensure that schema changes in the source data can be automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?

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

Answer: B

Explanation:
Databricks documents mergeSchema as the write option used to enable schema evolution when appending data to Delta tables. This allows new columns in the source to be automatically merged into the target schema rather than causing the write to fail. ( Databricks Documentation ) overwriteSchema is used with overwrite operations, not append-style schema evolution. validateSchema and ignoreChanges are not the correct configuration for this requirement. Because the pipeline is append-only and must tolerate source schema changes automatically, mergeSchema = true is the documented choice. ( Databricks Documentation )
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NEW QUESTION # 201
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control. The team decided to use Databricks system tables, available under the System catalog in Unity Catalog, to gain detailed visibility into workspace activity.
Which SQL query should the team run from the System catalog to achieve this?

A. SELECT sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B. SELECT sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
C. SELECT identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10
D. SELECT sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The system.billing.usage table in the Unity Catalog System schema provides detailed usage metrics for each workload in the workspace. The field identity_metadata.run_as identifies the user or service principal under which the job or query executed. Summing usage_quantity provides total DBU (Databricks Unit) consumption per user. According to Databricks documentation, this table is the authoritative source for monitoring workspace cost drivers, showing compute SKU, user, and DBU consumption over time. Grouping by identity_metadata.run_as and summing usage_quantity produces the correct aggregation to determine top users. Other queries use non-existent or incorrect fields (created_by, run_name, or multiplied usage quantities), which do not reflect actual billing metrics.

NEW QUESTION # 202
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. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
B. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
C. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
D. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Answer: B

Explanation:
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
* Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html
* DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table

NEW QUESTION # 203
A data engineer created a daily batch ingestion pipeline using a cluster with the latest DBR version to store banking transaction data, and persisted it in a MANAGED DELTA table called prod.gold.all_banking_transactions_daily. The data engineer is constantly receiving complaints from business users who query this table ad hoc through a SQL Serverless Warehouse about poor query performance. Upon analysis, the data engineer identified that these users frequently use high-cardinality columns as filters. The engineer now seeks to implement a data layout optimization technique that is incremental, easy to maintain, and can evolve over time.
Which command should the data engineer implement?

A. Alter the table to use Z-ORDER and implement a periodic OPTIMIZE command.
B. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
C. Alter the table to use Hive-Style Partitions and implement a periodic OPTIMIZE command.
D. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks recommends Liquid Clustering for optimizing data layout in large Delta tables where query filters involve high-cardinality columns. Liquid Clustering automatically manages file organization and supports incremental maintenance without the need to rewrite data when clustering keys evolve. This is a key advantage over static partitioning or Z-ordering, which require costly file rewrites whenever optimization keys change. By combining Liquid Clustering with a periodic OPTIMIZE command, Databricks automatically compacts small files and maintains efficient data skipping performance. As stated in the Delta Lake optimization guide, Liquid Clustering is designed for scalability, minimal maintenance, and adaptability for analytical workloads with evolving query patterns-making B the correct answer.

NEW QUESTION # 204
A data engineer wants to horizontally combine two tables as a part of a query. They want to use a shared
column as a key column, and they only want the query result to contain rows whose value in the key column is
present in both tables.
Which of the following SQL commands can they use to accomplish this task?

A. MERGE
B. INNER JOIN
C. OUTER JOIN
D. UNION
E. LEFT JOIN

Answer: B

NEW QUESTION # 205
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