100% Pass Databricks-Certified-Professional-Data-Engineer - Exam Databricks Certified Professional Data Engineer Exam Dumps

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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:Databricks
Exam Name:Databricks Certified Professional Data Engineer Exam
Exam Number:Databricks-Certified-Professional-Data-Engineer
Real Exam Qty:60-70
Exam Price:USD 200
Exam Duration:120 minutes
Certificate Validity Period:2 years
Passing Score:70%
Available Languages:English
Related Certifications:Databricks Certified Data Engineer Associate
Exam Format:Multiple choice, Scenario-based questions, Multiple select
Recommended Training:Databricks Academy
Exam Registration:Databricks Certification Portal
Sample Questions:Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:Online proctored exam
Pre Condition:Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform.
Official Syllabus URL:https://www.databricks.com/learn/certification

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Reliable Databricks-Certified-Professional-Data-Engineer Exam Syllabus, Interactive Databricks-Certified-Professional-Data-Engineer Questions

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Databricks Certified Professional Data Engineer exam is designed for data professionals who want to validate their skills in building and optimizing data pipelines on the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam is intended to demonstrate a comprehensive understanding of data engineering principles, best practices, and practical skills required for designing, building, and maintaining robust, scalable, and efficient data pipelines using Databricks. Databricks Certified Professional Data Engineer Exam certification exam is recognized globally and provides a competitive edge in the job market.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q191-Q196):

NEW QUESTION # 191
A particular job seems to be performing slower and slower over time, the team thinks this started to happen when a recent production change was implemented, you were asked to take look at the job history and see if we can identify trends and root cause, where in the workspace UI can you perform this analysis?

Answer: C

Explanation:
Explanation
The answer is,
Under jobs UI select the job you are interested, under runs we can see current active runs and last 60 days historical run


NEW QUESTION # 192
When building a DLT s pipeline you have two options to create a live tables, what is the main dif-ference between CREATE STREAMING LIVE TABLE vs CREATE LIVE TABLE?

Answer: A

Explanation:
Explanation
The answer is, CREATE STREAMING LIVE TABLE is used when working with Streaming data sources and Incremental data


NEW QUESTION # 193
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

Answer: E

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.


NEW QUESTION # 194
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?

Answer: B

Explanation:
Explanation
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so
P(E1|E2) = P(E1.E2)/P(E2)
P(E1,E2,E3)/P(E2,E3)
If the events are A and B respectively, this is said to be "the probability of A given B"
It is commonly denoted by P(A|B):or sometimes PB(A). In case that both "A" and "B" are categorical
variables, conditional probability table is typically used to represent the conditional probability.


NEW QUESTION # 195
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?

Answer: B

Explanation:
Explanation
The answer is Checkpointing and write-ahead logging.
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.


NEW QUESTION # 196
......

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