Databricks Databricks-Certified-Data-Engineer-Associate Exam Cram - Databricks-Certified-Data-Engineer-Associate Reliable Test Objectives

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To become a Databricks-Certified-Data-Engineer-Associate, candidates must pass a rigorous exam that tests their understanding of Databricks and its various components. Databricks-Certified-Data-Engineer-Associate Exam consists of multiple-choice questions and requires candidates to demonstrate their knowledge of data engineering best practices, data processing techniques, and machine learning algorithms. Successful candidates will receive a certification that recognizes their expertise in working with Databricks.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a professional certification program designed to assess the skills and knowledge of individuals working in the field of data engineering. Databricks Certified Data Engineer Associate Exam certification validates the ability of data engineers to design, build, and maintain data pipelines and data warehouses using Databricks technologies. Databricks-Certified-Data-Engineer-Associate exam covers a wide range of topics, including data ingestion, data transformation, data modeling, data warehousing, and data quality.

The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed for data engineers who want to demonstrate their expertise in building and managing data pipelines using Databricks. Databricks is a cloud-based platform that allows data engineers to easily build and manage data pipelines using Apache Spark. Databricks Certified Data Engineer Associate Exam certification exam covers a wide range of topics related to data engineering, including data pipeline architecture, data modeling, data ingestion, data processing, and data storage.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?

A. GRANT USAGE ON DATABASE customers TO team;
B. GRANT VIEW ON CATALOG customers TO team;
C. GRANT CREATE ON DATABASE team TO customers;
D. GRANT USAGE ON CATALOG team TO customers;
E. GRANT CREATE ON DATABASE customers TO team;

Answer: A

Explanation:
The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). Reference: GRANT, Privilege types, Securable objects, Principals

NEW QUESTION # 105
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 new webhook alert destination.
B. They can set up an Alert without notifications.
C. They can set up an Alert with a custom template.
D. They can set up an Alert with one-time notifications.
E. They can set up an Alert with a new email alert destination.

Answer: A

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 # 106
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can create a new job from scratch and add both tasks to run concurrently.
B. They can create a new task in the existing Job and then add it as a dependency of the original task.
C. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
D. They can clone the existing task to a new Job and then edit it to run the new notebook.
E. They can clone the existing task in the existing Job and update it to run the new notebook.

Answer: D

NEW QUESTION # 107
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Answer: C

Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

NEW QUESTION # 108
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
B. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
C. They could redesign the data model to separate the data used in the final query into a new table.
D. They could submit a feature request with Databricks to add this functionality.
E. They could only run the entire program on Sundays.

Answer: A

Explanation:
This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. Reference: PySpark SQL Module, Python datetime Module, Databricks Jobs

NEW QUESTION # 109
......

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DATABRICKS DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE EXAM CRAM - DATABRICKS-CERTIFIED-DATA-ENGINEER-ASSOCIATE RELIABLE TEST OBJECTIVES

Databricks Databricks-Certified-Data-Engineer-Associate Exam Cram - Databricks-Certified-Data-Engineer-Associate Reliable Test Objectives