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Databricks Certified Professional Data Engineer Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production.
How can the data engineer run unit tests against function that work with data in production?

A. Define and import unit test functions from a separate Databricks notebook
B. Define and unit test functions using Files in Repos
C. Run unit tests against non-production data that closely mirrors production
D. Define units test and functions within the same notebook

Answer: C

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.
References:
* Databricks Documentation on Testing: Testing and Validation of Data and Notebooks

NEW QUESTION # 67
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.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
B. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
C. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
D. 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.

Answer: D

Explanation:
The provided MERGE statement is a classic implementation of a Type 2 SCD in a data warehousing context.
In this approach, historical data is preserved by keeping old records (marking them as not current) and adding new records for changes. Specifically, when a match is found and there's a change in the address, the existing record in the customers table is updated to mark it as no longer current (current = false), and an end date is assigned (end_date = staged_updates.effective_date). A new record for the customer is then inserted with the updated information, marked as current. This method ensures that the full history of changes to customer information is maintained in the table, allowing for time-based analysis of customer data.References:
Databricks documentation on implementing SCDs using Delta Lake and the MERGE statement (https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using-merge).

NEW QUESTION # 68
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. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
B. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
D. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.

Answer: A

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.References:
* Databricks Documentation on Delta Lake: Delta Lake Guide
* Databricks Documentation on ACID Transactions in Delta Lake: ACID Transactions in Delta Lake

NEW QUESTION # 69
What is the main difference between the below two commands?
1.INSERT OVERWRITE table_name
2.SELECT * FROM table
1.CREATE OR REPLACE TABLE table_name
2.AS SELECT * FROM table

A. Both are same and results in identical outcomes
B. INSERT OVERWRITE replaces data by default, CREATE OR REPLACE replaces data and Schema by default
C. INSERT OVERWRITE clears historical data versions by de-fault, CREATE OR REPLACE maintains the historical data versions by default
D. INSERT OVERWRITE replaces data and schema by default, CREATE OR REPLACEreplaces data by default
E. INSERT OVERWRITE maintains historical data versions by de-fault, CREATE OR REPLACEclears the historical data versions by default

Answer: B

Explanation:
Explanation
The main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to overwrite schema, only when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command will fail.

NEW QUESTION # 70
A senior data engineer is planning large-scale data workflows. The task is to identify the considerations that form a foundation for creating scalable data models for managing large datasets. The team has listed Delta Lake capabilities and wants to determine which feature should not be considered as a core factor.
Which key feature can be ignored while evaluating Delta Lake?

A. Delta Lake's ability to process data in both batch and streaming modes seamlessly, providing flexibility in ingestion and processing.
B. Delta Lake provides limited support for monitoring and troubleshooting data pipelines, so relevant partner tools have to be identified and set up for enhanced operational efficiency.
C. Delta Lake optimizes metadata handling, efficiently managing billions of files and facilitating scalability to petabyte-scale datasets.
D. Delta Lake works with various data formats (Parquet, JSON, CSV) and integrates well with Spark and Databricks tools.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Databricks documentation emphasizes that Delta Lake provides robust capabilities for batch and streaming unification, scalable metadata management, and seamless integration with Databricks and Apache Spark. These are core design principles for building modern, scalable data platforms. However, the statement claiming that Delta provides "limited support for monitoring and troubleshooting" is not valid. Delta Lake itself includes comprehensive logging, event history tracking, and data lineage via the transaction log, and integrates with Databricks' built-in monitoring tools. Hence, such a limitation is not a design consideration when evaluating Delta Lake's scalability. Therefore, this statement (D) can be ignored, as it does not reflect Delta Lake's documented capabilities.

NEW QUESTION # 71
......

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