Latest Databricks Databricks-Certified-Data-Engineer-Professional Exam Questions in Three Formats

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
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 Hive-Style Partitions and implement a periodic OPTIMIZE command.
C. Alter the table to use Liquid Clustering and implement a periodic OPTIMIZE command.
D. Alter the table to use Hive-Style Partitions + Z-ORDER and implement a periodic OPTIMIZE command.

Answer: C

Explanation:
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 # 78
A view is registered with the following code:

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

A. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
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. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.
D. 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.

Answer: D

NEW QUESTION # 79
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.

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

A. Yes; the change data feed uses foreign keys to ensure delete consistency throughout the Lakehouse.
B. No; the change data feed only tracks inserts and updates not deleted records.
C. No; files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
D. No; the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
E. Yes; Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.

Answer: C

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
The DELETE operation in Delta Lake is ACID compliant, which means that once the operation is successful, the records are logically removed from the table. However, the underlying files that contained these records may still exist and be accessible via time travel to older versions of the table. To ensure that these records are physically removed and compliance with GDPR is maintained, a VACUUM command should be used to clean up these data files after a certain retention period. The VACUUM command will remove the files from the storage layer, and after this, the records will no longer be accessible.

NEW QUESTION # 80
A Data Engineer is building a simple data pipeline using Lakeflow Declarative Pipelines (LDP) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create Lakeflow Declarative Pipelines that read the raw JSON data and write it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using LDP?

A. import dlt
@dlt.table
def raw_customers():
return spark.read.json("s3://my-bucket/raw-customers/")
B. import dlt
@dlt.table
def raw_customers():
return spark.read.format("parquet").load("s3://my-bucket/raw-customers/")
C. import dlt
@dlt.table
def raw_customers():
return spark.read.format("csv").load("s3://my-bucket/raw-customers/")
D. import dlt
@dlt.view
def raw_customers():
return spark.format.json("s3://my-bucket/raw-customers/")

Answer: A

Explanation:
The correct method to define a table using Lakeflow Declarative Pipelines (LDP) is with the
@dlt.table decorator, which persists the output as a managed Delta table. When ingesting raw JSON data, spark.read.json() or spark.read.format("json").load() is the standard approach. This reads JSON- formatted files from the source and stores them in Delta format automatically managed by Databricks.

NEW QUESTION # 81
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 Lake delete command only provides ACID guarantees when combined with the merge into command.
B. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
C. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
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.

NEW QUESTION # 82
......

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Latest Databricks Databricks-Certified-Data-Engineer-Professional Exam Questions in Three Formats

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q77-Q82):

Databricks-Certified-Data-Engineer-Professional Exam Questions - Databricks Certified Data Engineer Professional Exam Test Questions & Databricks-Certified-Data-Engineer-Professional Test Guide

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