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Databricks is a powerful data engineering and machine learning platform that is widely used across various industries. To ensure that professionals have the necessary skills and expertise to work with Databricks, the company offers a certification program. One of the certifications offered is the Databricks Certified Professional Data Engineer exam, also known as the Databricks Databricks-Certified-Professional-Data-Engineer Exam.

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Databricks Certified Professional Data Engineer exam is a certification program designed for data professionals who want to validate their expertise in building and maintaining data pipelines using Databricks. Databricks is a cloud-based data engineering platform that provides a unified analytics engine for big data processing, machine learning, and streaming analytics. Databricks-Certified-Professional-Data-Engineer Exam is designed to test a candidate's ability to design, build, and optimize data pipelines using Databricks, as well as their proficiency in data modeling, data warehousing, and data integration.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q124-Q129):

NEW QUESTION # 124
A Delta Lake table was created with the below query:

Consider the following query:
DROP TABLE prod.sales_by_store -
If this statement is executed by a workspace admin, which result will occur?

A. Nothing will occur until a COMMIT command is executed.
B. Data will be marked as deleted but still recoverable with Time Travel.
C. The table will be removed from the catalog but the data will remain in storage.
D. An error will occur because Delta Lake prevents the deletion of production data.
E. The table will be removed from the catalog and the data will be deleted.

Answer: E

Explanation:
Explanation
When a table is dropped in Delta Lake, the table is removed from the catalog and the data is deleted. This is because Delta Lake is a transactional storage layer that provides ACID guarantees. When a table is dropped, the transaction log is updated to reflect the deletion of the table and the data is deleted from the underlying storage. References:
https://docs.databricks.com/delta/quick-start.html#drop-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-table

NEW QUESTION # 125
Which of the following SQL keywords can be used to append new rows to an existing Delta table?

A. UNION
B. COPY
C. INSERT INTO
D. DELETE
E. UPDATE

Answer: C

NEW QUESTION # 126
Which of the following type of tasks cannot setup through a job?

A. DELTA LIVE PIPELINE
B. Databricks SQL Dashboard refresh
C. Spark Submit
D. Python
E. Notebook

Answer: B

NEW QUESTION # 127
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 new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
B. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
C. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
D. The update throws an error because changes to existing columns in the target schema are not supported.

Answer: A

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.
Reference:
Databricks documentation on schema evolution in Delta Lake: https://docs.databricks.com/delta/delta-batch.html#schema-evolution

NEW QUESTION # 128
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings.
The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
C. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
D. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

Answer: C

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.References: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 129
......

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Leo Webb Leo Webb

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