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Exams Databricks-Certified-Professional-Data-Engineer Torrent - New Databricks-Certified-Professional-Data-Engineer Exam Pass4sure

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Databricks Certified Professional Data Engineer exam measures a candidate's ability to design, build, and manage data pipelines using Databricks. It covers a wide range of topics, including data ingestion, transformation, storage, and analysis. Candidates must demonstrate their proficiency in using Databricks tools and techniques to solve real-world data engineering problems. Databricks Certified Professional Data Engineer Exam certification exam is ideal for data engineers who want to validate their skills and expertise in using Databricks to build and manage data pipelines.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q28-Q33):

NEW QUESTION # 28
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:
Which limitation will the team face while diagnosing this problem?

A. New fields not be computed for historic records.
B. Updating the table schema requires a default value provided for each file added.
C. Spark cannot capture the topic partition fields from the kafka source.
D. Updating the table schema will invalidate the Delta transaction log metadata.

Answer: A

Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.
References:
* Databricks documentation on Delta Lake schema management:
https://docs.databricks.com/delta/delta-batch.html#schema-management

NEW QUESTION # 29
You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex,
Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of
the clusters, you notice that there is significant overlap between the clusters. What should you do?

A. Decrease the number of clusters
B. Increase the number of clusters
C. Identify additional measures to add to the analysis
D. Remove one of the measures

Answer: A

NEW QUESTION # 30
How VACCUM and OPTIMIZE commands can be used to manage the DELTA lake?

A. OPTIMIZE command can be used to compact small parquet files, and the VAC-CUM command can be used to delete parquet files that are marked for deletion/unused.
(Correct)
B. VACCUM command can be used to delete empty/blank parquet files in a delta table. OPTIMIZE command can be used to update stale statistics on a delta table.
C. VACCUM command can be used to compact small parquet files, and the OP-TIMZE command can be used to delete parquet files that are marked for dele-tion/unused.
D. VACCUM command can be used to compress the parquet files to reduce the size of the table, OPTIMIZE command can be used to cache frequently delta tables for better performance.
E. VACCUM command can be used to delete empty/blank parquet files in a delta table, OPTIMIZE command can be used to cache frequently delta tables for better perfor-mance.

Answer: A

Explanation:
Explanation
VACCUM:
You can remove files no longer referenced by a Delta table and are older than the retention thresh-old by running the vacuum command on the table. vacuum is not triggered automatically. The de-fault retention threshold for the files is 7 days. To change this behavior, see Configure data reten-tion for time travel.
OPTIMIZE:
Using OPTIMIZE you can compact data files on Delta Lake, this can improve the speed of read queries on the table. Too many small files can significantly degrade the performance of the query.

NEW QUESTION # 31
A newly joined team member John Smith in the Marketing team who currently does not have any access to the data requires read access to customers table, which of the following statements can be used to grant access.

A. GRANT READ, USAGE ON customers TO john.smith@marketing.com
B. GRANT SELECT, USAGE TO john.smith@marketing.com ON TABLE customers
C. GRANT SELECT, USAGE ON TABLE customers TO john.smith@marketing.com
D. GRANT READ, USAGE ON TABLE customers TO john.smith@marketing.com
E. GRANT READ, USAGE TO john.smith@marketing.com ON TABLE customers

Answer: C

Explanation:
Explanation
The answer is GRANT SELECT, USAGE ON TABLE customers TO john.smith@marketing.com Data object privileges - Azure Databricks | Microsoft Docs

NEW QUESTION # 32
A table named user_ltv is being used to create a view that will be used by data analysis on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:

An analyze who is not a member of the auditing group executing the following query:

Which result will be returned by this query?

A. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
B. All records from all columns will be displayed with the values in user_ltv.
C. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
D. All age values less than 18 will be returned as null values all other columns will be returned with the values in user_ltv.

Answer: A

Explanation:
Given the CASE statement in the view definition, the result set for a user not in the auditing group would be constrained by the ELSE condition, which filters out records based on age. Therefore, the view will return all columns normally for records with an age greater than 18, as users who are not in the auditing group will not satisfy the is_member('auditing') condition. Records not meeting the age > 18 condition will not be displayed.

NEW QUESTION # 33
......

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