Open the auxiliary access

Firefly Open Source Community

   Login   |   Register   |
New_Topic
Firefly Open Source Community › Embedded Board › AIO-3568J› Quiz Databricks - Databricks-Certified-Professional- ...
New Topic
Print Previous Topic Next Topic

[General] Quiz Databricks - Databricks-Certified-Professional-Data-Engineer–Efficient Prac

leoshaw967

129

Credits

0

Prestige

0

Contribution

registered members

Rank: 2

Credits
129

【General】 Quiz Databricks - Databricks-Certified-Professional-Data-Engineer–Efficient Prac

Posted at yesterday 11:23      View：7 | Replies：0        Print      Only Author   [Copy Link] 1#
Have similar features to the desktop-based exam simulator Contains actual Databricks Databricks-Certified-Professional-Data-Engineer practice test that will help you grasp every topic Compatible with every operating system. Does not require any special plugins to operate. Creates a Databricks-Certified-Professional-Data-Engineer Exam atmosphere making candidates more confident. Keeps track of your progress with self-analysis and Points out mistakes at the end of every attempt.
Databricks Certified Professional Data Engineer certification is a valuable credential for data engineers who want to demonstrate their expertise in using the Databricks platform. It provides employers with a way to identify and verify the skills of candidates and employees, and it can help data engineers advance their careers by demonstrating their proficiency in using the Databricks platform to build and maintain scalable and reliable data pipelines.
To become a Databricks Certified Professional Data Engineer, candidates must pass a rigorous exam that covers a wide range of topics related to data engineering. Databricks-Certified-Professional-Data-Engineer Exam is designed to test a candidate's ability to design, build, and maintain data pipelines using Databricks tools and technologies. It also tests their understanding of data modeling, data warehousing, and data integration.
Quiz Reliable Databricks-Certified-Professional-Data-Engineer - Databricks Certified Professional Data Engineer Exam Practice ExamsAs for Databricks Databricks-Certified-Professional-Data-Engineer exam, it is the most difficult to pass. But, as long as you believe in Exam4Tests, everything is ok. Exam4Tests Databricks Databricks-Certified-Professional-Data-Engineer exam simulations contain the most accurate questions and answers. If you don't believe our Databricks Databricks-Certified-Professional-Data-Engineer certification training, you can go to our Exam4Tests. You can find pdf real questions and answers and download it. And the purchase rate is unbelievably high every day. By choosing it, pass rate is 100%. Hurry up! Don't hesitate to add our Databricks Databricks-Certified-Professional-Data-Engineer Dumps Torrent to your shopping cart.
Databricks is a leading company in the field of data engineering, providing a cloud-based platform for collaborative data analysis and processing. The company's platform is used by a wide range of companies and organizations, including Fortune 500 companies, government agencies, and academic institutions. Databricks offers a range of certifications to help professionals demonstrate their proficiency in using the platform, including the Databricks Certified Professional Data Engineer certification.
Databricks Certified Professional Data Engineer Exam Sample Questions (Q91-Q96):NEW QUESTION # 91
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 update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
  • B. The update throws an error because changes to existing columns in the target schema are not supported.
  • C. 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.
  • D. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
Answer: C
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.
References:
* Databricks documentation on schema evolution in Delta Lake:
https://docs.databricks.com/delt ... ml#schema-evolution

NEW QUESTION # 92
Given the following error traceback:
AnalysisException: cannot resolve 'heartrateheartrateheartrate' given input columns:
[spark_catalog.database.table.device_id, spark_catalog.database.table.heartrate, spark_catalog.database.table.mrn, spark_catalog.database.table.time] The code snippet was:
display(df.select(3*"heartrate"))
Which statement describes the error being raised?
  • A. There is no column in the table named heartrateheartrateheartrate.
  • B. There is a type error because a column object cannot be multiplied.
  • C. There is a syntax error because the heartrate column is not correctly identified as a column.
  • D. There is a type error because a DataFrame object cannot be multiplied.
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "select() expects column names or Column expressions."
Exact extract: "When using strings directly, Spark SQL interprets them as literal column names." Exact extract: "Python string operations, such as "colname"*3, return repeated strings, not column expressions." The expression 3*"heartrate" is Python string multiplication, which evaluates to "heartrateheartrateheartrate". The select() method interprets this as a literal column name. Since there is no column with that name in the DataFrame schema, Spark raises AnalysisException saying it cannot resolve that column. To correctly multiply a column by a scalar, one must use the column expression form:
from pyspark.sql.functions import col
df.select((col("heartrate") * 3).alias("heartrate_x3"))
This ensures Spark evaluates the arithmetic operation on the column instead of misinterpreting the string.

NEW QUESTION # 93
A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data.
Which statement is applicable for Lakehouse Federations to maintain data consistency?
  • A. Federation creates local copies that must be manually refreshed.
  • B. Federation implements change data capture (CDC) from all sources.
  • C. Federation provides read-only access that reflects the current state of source systems.
  • D. A separate data synchronization service must be deployed.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Lakehouse Federation allows Databricks to query and manage external data sources through a single governance layer, without moving or copying data. The documentation specifies that "Federated queries provide read-only access to data, reflecting the current state of the underlying source system." This ensures consistency across teams since all users access the same source of truth directly from the external system through Unity Catalog. Federation does not perform CDC replication or local caching; it queries live data on demand. Hence, option A accurately represents how Lakehouse Federation maintains consistency across federated sources.

NEW QUESTION # 94
Which of the following SQL keywords can be used to append new rows to an existing Delta table?
  • A. DELETE
  • B. INSERT INTO
  • C. UNION
  • D. COPY
  • E. UPDATE
Answer: B

NEW QUESTION # 95
An external object storage container has been mounted to the location /mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:
After the database was successfully created and permissions configured, a member of the finance team runs the following code:
If all users on the finance team are members of the finance group, which statement describes how the tx_sales table will be created?
  • A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
  • B. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
  • C. An external table will be created in the storage container mounted to /mnt/finance eda bucket.
  • D. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
  • E. A managed table will be created in the DBFS root storage container.
Answer: A
Explanation:
https://docs.databricks.com/en/lakehouse/data-objects.html

NEW QUESTION # 96
......
Databricks-Certified-Professional-Data-Engineer Certification Training: https://www.exam4tests.com/Databricks-Certified-Professional-Data-Engineer-valid-braindumps.html
https://www.deutschpruefung.com
Reply

Use props Report

Return to List
High mode
B Color Image Link Quote Code Smilies
You need to log in before you can reply Login | Register

This forum Credits Rules

  •  

Service Hotline : (+86)18688117175
Contact us
E-mail : global@t-firefly.com
Copyright © 2014-2022, FIREFLY TECHNOLOGY CO.,LTD. All Rightts Reserved. 粤ICP备14022046号-2
Quick Reply Back to top Back to list