TEST DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE SAMPLE ONLINE - VCE DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE FREE

Test Databricks-Certified-Data-Engineer-Associate Sample Online - Vce Databricks-Certified-Data-Engineer-Associate Free

Test Databricks-Certified-Data-Engineer-Associate Sample Online - Vce Databricks-Certified-Data-Engineer-Associate Free

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a comprehensive certification program designed to validate the skills and knowledge of data engineers in using Databricks to build and manage data pipelines, perform data analysis, and develop data-driven solutions. Databricks-Certified-Data-Engineer-Associate Exam is designed for professionals who work with big data and are responsible for designing, building, and maintaining data pipelines using Databricks.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed for individuals who are interested in pursuing a career in data engineering. Databricks Certified Data Engineer Associate Exam certification exam is developed in collaboration with Databricks, a leading data and AI company that provides a unified analytics platform for data engineering, data science, and machine learning. The Databricks-Certified-Data-Engineer-Associate Exam Tests candidates on their knowledge of data engineering principles, practices, and tools.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

  • A. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
  • B. GRANT SELECT ON TABLE sales TO team;
  • C. GRANT ALL PRIVILEGES ON TABLE team TO sales;
  • D. GRANT USAGE ON TABLE sales TO team;
  • E. GRANT ALL PRIVILEGES ON TABLE sales TO team;

Answer: E

Explanation:
To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON TABLE statement. This statement will grant all the possible privileges on the table, such as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on the table team to the user or group sales, which is the opposite of what the question asks. References: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a table using SQL - Azure Databricks, SQL Privileges - Databricks


NEW QUESTION # 44
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

  • A. spark.delta.table("sales")
  • B. SELECT * FROM sales
  • C. There is no way to share data between PySpark and SQL.
  • D. spark.sql("sales")
  • E. spark.table("sales")

Answer: A


NEW QUESTION # 45
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

  • A. Simplified governance
  • B. Cloud-specific integrations
  • C. Ability to scale storage
  • D. Avoiding vendor lock-in
  • E. Ability to scale workloads

Answer: D

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the Databricks Lakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). Reference: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.


NEW QUESTION # 46
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 cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

  • A. trigger()
  • B. trigger("5 seconds")
  • C. trigger(processingTime="5 seconds")
  • D. trigger(once="5 seconds")
  • E. trigger(continuous="5 seconds")

Answer: C

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.


NEW QUESTION # 47
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

  • A. They can set up an Alert with a custom template.
  • B. They can set up an Alert with a new email alert destination.
  • C. They can set up an Alert with a new webhook alert destination.
  • D. They can set up an Alert with one-time notifications.
  • E. They can set up an Alert without notifications.

Answer: C

Explanation:
A webhook alert destination is a notification destination that allows Databricks to send HTTP POST requests to a third-party endpoint when an alert is triggered. This enables the data engineer to integrate Databricks alerts with their preferred messaging or collaboration platform, such as Slack, Microsoft Teams, or PagerDuty.
To set up a webhook alert destination, the data engineer needs to create and configure a webhook connector in their messaging platform, and then add the webhook URL to the Databricks notification destination. After that, the data engineer can create an alert for their Databricks SQL query, and select the webhook alert destination as the notification destination. The alert can be configured with a custom condition, such as when the number of stores with $0 in sales is greater than zero, and a custom message template, such as "Alert:
{number_of_stores} stores have $0 in sales". The alert can also be configured with a recurrence interval, such as every hour, to check the query result periodically. When the alert condition is met, the data engineer and their team will receive a notification via the messaging webhook, with the custom message and a link to the Databricks SQL query. The other options are either not suitable for sending notifications via a messaging webhook (A, B, E), or not suitable for sending recurring notifications . References: Databricks Documentation -Manage notification destinations, Databricks Documentation - Create alerts for Databricks SQL queries, Databricks Documentation - Configure alert conditions and messages.


NEW QUESTION # 48
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