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Databricks Certified Professional Data Engineer exam is designed to test the knowledge and skills of data professionals who use Databricks for data engineering tasks. Databricks-Certified-Professional-Data-Engineer exam covers a range of topics, including data ingestion, data transformation, data storage, and data analysis. Databricks-Certified-Professional-Data-Engineer exam also tests candidates' ability to use Databricks tools and services to perform these tasks effectively.

The Databricks Certified Professional Data Engineer Exam certification verifies that the candidate has significant experience in implementing big data solutions that operate on the Databricks Delta Architecture. To earn the certification, a candidate must pass a 90-minute exam consisting of up to 50 multiple-choice and multiple-select questions. The Databricks-Certified-Professional-Data-Engineer Certification is valid for two years from the date of passing the exam.

Databricks Databricks-Certified-Professional-Data-Engineer certification is a valuable credential for professionals working with big data and data engineering. Databricks Certified Professional Data Engineer Exam certification validates the candidates’ technical skills in working with big data projects implemented on the Databricks platform. It aims to create a standard for big data engineering skills and provides a valuable addition to a candidate's resume. Earning this certification opens up doors for career advancement and can improve a professional's ability to secure a high-paying job in the big data industry.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

A. Iterating through a stored secret and printing each character will display secret contents in plain text.
B. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.
C. Account administrators can see all secrets in plain text by logging on to the Databricks Accounts console.
D. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
E. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.

Answer: D

Explanation:
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text. Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "List secrets" section.

NEW QUESTION # 41
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

A. Ridge and Lasso
B. Apriori Algorithm
C. Ordinary Least squares
D. Integer programming

Answer: C

Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters
are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age
and education. In other words, the fitted model should minimize the overall error between the linear model and
the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters

NEW QUESTION # 42
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
B. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
C. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
D. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
E. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.

Answer: A

Explanation:
Explanation
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. References:
Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table

NEW QUESTION # 43
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of the Job's schedule and configuration?

A. They can submit the Job once on a Job cluster.
B. They can download the XML description of the Job from the Job's page
C. They can link the Job to notebooks that are a part of a Databricks Repo.
D. They can submit the Job once on an all-purpose cluster.
E. They can download the JSON equivalent of the job from the Job's page.

Answer: D

NEW QUESTION # 44
Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?

A. /jobs/runs/get-output
B. /jobs/runs/get
C. /jobs/list
D. /jobs/get
E. /jobs/runs/list

Answer: D

Explanation:
This is the correct answer because it is the REST API call that can be used to review the notebooks configured to run as tasks in a multi-task job. The REST API is an interface that allows programmatically interacting with Databricks resources, such as clusters, jobs, notebooks, or tables. The REST API uses HTTP methods, such as GET, POST, PUT, or DELETE, to perform operations on these resources. The /jobs/get endpoint is a GET method that returns information about a job given its job ID. The information includes the job settings, such as the name, schedule, timeout, retries, email notifications, and tasks. The tasks are the units of work that a job executes. A task can be a notebook task, which runs a notebook with specified parameters; a jar task, which runs a JAR uploaded to DBFS with specified main class and arguments; or a python task, which runs a Python file uploaded to DBFS with specified parameters. A multi-task job is a job that has more than one task configured to run in a specific order or in parallel. By using the /jobs/get endpoint, one can review the notebooks configured to run as tasks in a multi-task job. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Jobs" section; Databricks Documentation, under "Get" section; Databricks Documentation, under "JobSettings" section.

NEW QUESTION # 45
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