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The Databricks Databricks-Certified-Professional-Data-Engineer Exam is designed to test the candidate's ability to work with Databricks in a real-world setting. Candidates are required to demonstrate their ability to design and implement data pipelines that are scalable, efficient, and reliable. They must also be able to troubleshoot issues that arise during the data engineering process and optimize performance to ensure that pipelines run smoothly.

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The Databricks Certified Professional Data Engineer Exam certification exam is a computer-based test that consists of multiple-choice questions. Candidates have two hours to complete the exam, and they must achieve a minimum score of 70% to pass. Databricks-Certified-Professional-Data-Engineer Exam is proctored, and candidates must have a reliable internet connection and a computer with a webcam and microphone to take the test.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q172-Q177):

NEW QUESTION # 172
A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
Theuser_ltvtable has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Three columns will be returned, but one column will be named "redacted" and contain only null values.
B. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
C. The email and ltv columns will be returned with the values in user itv.
D. Only the email and itv columns will be returned; the email column will contain all null values.
E. The email, age. and ltv columns will be returned with the values in user ltv.

Answer: B

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

NEW QUESTION # 173
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the skipChangeCommits flag to true on bpm_stats
B. Set the pipelines, reset, allowed property to false on raw_iot
C. Set the SkipChangeCommits flag to true raw_lot
D. Set the pipelines, reset, allowed property to false on bpm_stats

Answer: B

NEW QUESTION # 174
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
B. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
D. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
E. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

Answer: D

NEW QUESTION # 175
Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum
likelihood estimate for the proportion of "pink" items in the test set of color balls?

A. 24 0
B. .24
C. 4.8
D. 2.4
E. .48

Answer: B

Explanation:
Explanation
Given no additional information, the MLE for the probability of an item in the test set is exactly its frequency
in the training set. The method of maximum likelihood corresponds to many well-known estimation methods
in statistics. For example, one may be interested in the heights of adult female penguins, but be unable to
measure the height of every single penguin in a population due to cost or time constraints. Assuming that the
heights are normally (Gaussian) distributed with some unknown mean and variance, the mean and variance
can be estimated with MLE while only knowing the heights of some sample of the overall population. MLE
would accomplish this by taking the mean and variance as parameters and finding particular parametric values
that make the observed results the most probable (given the model).
In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects
the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes
the "agreement" of the selected model with the observed data, and for discrete random variables it indeed
maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood
estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution
and many other problems. However in some complicated problems, difficulties do occur: in such problems,
maximum-likelihood estimators are unsuitable or do not exist.

NEW QUESTION # 176
When you drop a managed table using SQL syntax DROP TABLE table_name how does it impact metadata, history, and data stored in the table?

A. Drops table from meta store and data from storage but keeps metadata and history in storage
B. Drops table and history but keeps meta data and data in storage
C. Drops table from meta store, drops metadata, history, and data in storage.
D. Drops table from meta store, meta data and history but keeps the data in storage
E. Drops table but keeps meta data, history and data in storage

Answer: C

Explanation:
Explanation
For a managed table, a drop command will drop everything from metastore and storage.
See the below image to understand the differences between dropping an external table.
Diagram Description automatically generated

NEW QUESTION # 177
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