Are Databricks Databricks-Certified-Data-Engineer-Associate Actual Questions Effective to Get Certified?

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q166-Q171):

NEW QUESTION # 166
A data engineer wants to create a new table containing the names of customers who live in France.
They have written the following command:
CREATE TABLE customersInFrance
_____ AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (Pll).
Which line of code fills in the above blank to successfully complete the task?

Answer: C

Explanation:
To include a property indicating that a table contains personally identifiable information (PII), the TBLPROPERTIES keyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows:
CREATE TABLE customersInFrance
USING DELTA
TBLPROPERTIES ('PII' = 'true')
AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
The TBLPROPERTIES ('PII' = 'true') line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information.
References:Databricks documentation on Delta Lake: Delta Lake on Databricks


NEW QUESTION # 167
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

Answer: E

Explanation:
Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. Reference: Delta Live Tables Overview, Data Quality Monitoring


NEW QUESTION # 168
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

Answer: B


NEW QUESTION # 169
A data engineer streams customer orders into a Kafka topic (orders_topic) and is currently writing the ingestion script of a DLT pipeline. The data engineer needs to ingest the data from Kafka brokers to DLT using Databricks What is the correct code for ingesting the data?

Answer: B


NEW QUESTION # 170
A data engineer is using the OPTIMIZE command on a Delta table. What happens when OPTIMIZE is run twice on the same table with the same data?

Answer: D

Explanation:
The OPTIMIZE command in Delta Lake is designed to compact small files into larger files to improve query performance and reduce file system overhead. Importantly, OPTIMIZE is considered an idempotent operation when run on unchanged data. This means that once files have been compacted optimally, running OPTIMIZE again does not produce additional changes unless new small files have been added to the table.
Databricks ensures that file compaction is efficient and avoids unnecessary rewrites, which helps control compute costs and maintain stable performance. While features like ZORDER can be applied during OPTIMIZE to improve data skipping, re-running the same command without new data will not further reorganize files. Options A, B, and C incorrectly imply additional restructuring or changes on repeated execution. According to Delta Lake best practices, OPTIMIZE should be run periodically after data ingestion events rather than repeatedly on unchanged datasets. This behavior reinforces predictable performance and efficient resource utilization in Databricks environments.
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NEW QUESTION # 171
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