Databricks Databricks-Certified-Professional-Data-Engineer Certification Exam Questions in 3 User-Friendly Formats

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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
Exam Format:	Multiple Choice
Exam Price:	USD 200
Available Languages:	Korean, Japanese, Portuguese (Brazil), English
Passing Score:	70%
Related Certifications:	Databricks Certified Associate Data Engineer
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Real Exam Qty:	59
Recommended Training:	Databricks Data Engineer Professional Training
Exam Registration:	Databricks Official Certification Registration
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored or onsite test center
Pre Condition:	No mandatory prerequisites; 1+ year hands-on experience and related training highly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-engineer-professional

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which of the following data workloads will utilize a gold table as its source?

A. A job that ingests raw data from a streaming source into the Lakehouse
B. A job that enriches data by parsing its timestamps into a human-readable format
C. A job that queries aggregated data that already feeds into a dashboard
D. A job that cleans data by removing malformatted records
E. A job that aggregates cleaned data to create standard summary statistics

Answer: C

Explanation:
Explanation
The answer is, A job that queries aggregated data that already feeds into a dashboard The gold layer is used to store aggregated data, which are typically used for dashboards and reporting.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

NEW QUESTION # 45
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.
B. The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
C. An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
D. A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
E. No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.

Answer: B

Explanation:
This is the correct answer because it describes what will occur when this code is executed. The code uses three Delta Lake tables as input sources: accounts, orders, and order_items. These tables are joined together using SQL queries to create a view called new_enriched_itemized_orders_by_account, which contains information about each order item and its associated account details. Then, the code uses write.format("delta").mode("overwrite") to overwrite a target table called enriched_itemized_orders_by_account using the data from the view. This means that every time this code is executed, it will replace all existing data in the target table with new data based on the current valid version of data in each of the three input tables. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Write to Delta tables" section.

NEW QUESTION # 46
The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?

A. The globally unique ID of the newly triggered run.
B. The number of times the job definition has been run in this workspace.
C. The job_id and number of times the job has been run are concatenated and returned.
D. The job_id is returned in this field.

Answer: A

Explanation:
* Exact extract: "run_id: The canonical identifier of a run."
References: Databricks Jobs API/CLI response fields.

NEW QUESTION # 47
A data engineer has ingested a JSON file into a table raw_table with the following schema:
1.transaction_id STRING,
2.payload ARRAY<customer_id:STRING, date:TIMESTAMP, store_id:STRING>
The data engineer wants to efficiently extract the date of each transaction into a table with the fol-lowing
schema:
1.transaction_id STRING,
2.date TIMESTAMP
Which of the following commands should the data engineer run to complete this task?

A. 1.SELECT transaction_id, explode(payload)
2.FROM raw_table;
B. 1.SELECT transaction_id, date
2.FROM raw_table;
C. 1.SELECT transaction_id, date from payload
2.FROM raw_table;
D. 1.SELECT transaction_id, payload[date]
2.FROM raw_table;
E. 1.SELECT transaction_id, payload.date
2.FROM raw_table;

Answer: E

NEW QUESTION # 48
Operations team is using a centralized data quality monitoring system, a user can publish data quality metrics through a webhook, you were asked to develop a process to send messages using a webhook if there is atleast one duplicate record, which of the following approaches can be taken to integrate an alert with current data quality monitoring system

A. Setup an alert to send an email, use python to parse email, and publish a webhook message
B. Setup an alert with custom template
C. Setup an alert with dynamic template
D. Setup an alert with custom Webhook destination
E. Use notebook and Jobs to use python to publish DQ metrics

Answer: D

Explanation:
Explanation
Alerts supports multiple destinations, email is the default destination.
Alert destinations | Databricks on AWS
Graphical user interface, application Description automatically generated

NEW QUESTION # 49
......

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