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Dumps Databricks-Certified-Professional-Data-Engineer Reviews, Databricks-Certified-Professional-Data-Engineer Latest Exam Question

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Databricks-Certified-Professional-Data-Engineer exam is a specialized test that focuses on assessing the technical skillsets of candidates in working on cloud-based big data projects. Candidates will be required to demonstrate their proficiency in a wide range of topics, including data structures and algorithms, distributed systems, database design, Hadoop and Spark, and machine learning. Databricks-Certified-Professional-Data-Engineer exam contains multiple-choice questions that test the candidates’ knowledge of these areas.

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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 Sample Questions (Q78-Q83):

NEW QUESTION # 78
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

A. Specify a new checkpointlocation
B. Increase the shuffle partitions to account for additional aggregates
C. Remove .option (mergeSchema', true') from the streaming write
D. Run REFRESH TABLE delta, /item_agg'

Answer: A

Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.
Reference:
Databricks documentation on Structured Streaming: https://docs.databricks.com/spark/latest/structured-streaming/index.html Databricks documentation on streaming checkpoints: https://docs.databricks.com/spark/latest/structured-streaming/production.html#checkpointing

NEW QUESTION # 79
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. fs
B. jobs
C. workspace
D. libraries
E. configure

Answer: D

Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl
dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl
This will upload the custom Python Wheel to the cluster and make it available for use with a production job.
You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
References:
* Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html
* Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations
* Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html

NEW QUESTION # 80
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 units test and functions within the same notebook
B. Define and import unit test functions from a separate Databricks notebook
C. Define and unit test functions using Files in Repos
D. Run unit tests against non-production data that closely mirrors production

Answer: D

NEW QUESTION # 81
What is the type of table created when you issue SQL DDL command CREATE TABLE sales (id int, units int)

A. Managed Parquet table
B. External Table
C. Query fails due to missing location
D. Query fails due to missing format
E. Managed Delta table

Answer: E

Explanation:
Explanation
Answer is Managed Delta table
Anytime a table is created without the Location keyword it is considered a managed table, by de-fault all managed tables DELTA tables Syntax CREATE TABLE table_name ( column column_data_type...)

NEW QUESTION # 82
When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?

A. CACHE TABLE table_name
B. UNCACHE TABLE table_name
C. REFRESH TABLE table_name
D. BROADCAST TABLE table_name
E. CLEAR CACH table_name

Answer: C

Explanation:
Explanation
The answer is REFRESH TABLE table_name
REFRESH TABLE table_name will force Spark to refresh the availability of external files and any changes.
When spark queries an external table it caches the files associated with it, so that way if the table is queried again it can use the cached files so it does not have to retrieve them again from cloud object storage, but the drawback here is that if new files are available Spark does not know until the Refresh command is ran.

NEW QUESTION # 83
......

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q78-Q83):

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Matt Davis Matt Davis

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Dumps Databricks-Certified-Professional-Data-Engineer Reviews, Databricks-Certified-Professional-Data-Engineer Latest Exam Question

The latest Databricks certification Databricks-Certified-Professional-Data-Engineer exam practice questions and answers

Databricks Certified Professional Data Engineer Exam Sample Questions (Q78-Q83):

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