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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Productionizing Data Pipelines	18%	- Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management - Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery - Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles
Development and Ingestion	30%	- Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL - Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Connecting to external data sources 3. Batch and streaming ingestion
Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Compute options: clusters, SQL warehouses, serverless 2. Workspace navigation and management 3. Data layout and optimization: partitioning, file sizing, caching
Data Processing & Transformations	31%	- Query optimization and performance 1. Understanding query plans 2. Optimization strategies - Data transformation techniques 1. DataFrame operations and transformations 2. Complex data processing logic 3. Aggregations, joins, and window functions
Data Governance & Quality	11%	- Unity Catalog implementation 1. Permissions and access control 2. Data governance model - Data quality and reliability 1. Schema enforcement and evolution 2. Data validation and quality checks

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q149-Q154):

NEW QUESTION # 149
A data engineer needs to enforce row-level security on main.secure.events(region STRING, event_id STRING). Members of the account group all_regions need to see all rows. All other users need to see only rows where region = 'EU'. Which SQL sequence satisfies the requirement?

Answer: A

Explanation:
The Boolean UDF must be created first and then assigned to the table using SET ROW FILTER ... ON. It returns TRUE for every row when the user belongs to all_regions; otherwise, it returns TRUE only for EU rows.

NEW QUESTION # 150
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

A. MERGE
B. IGNORE
C. INSERT
D. DROP
E. APPEND

Answer: A

Explanation:
The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. References: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE
| Databricks on AWS

NEW QUESTION # 151
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 one-time notifications.
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 a new email alert destination.
E. They can set up an Alert with a new webhook alert destination.

Answer: E

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 # 152
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
B. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
C. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
D. They can set the query's refresh schedule to end on a certain date in the query scheduler.
E. They can set the query's refresh schedule to end after a certain number of refreshes.

Answer: D

NEW QUESTION # 153
A data engineer needs Task C to run only when Task A succeeds and Task B fails.
Which dependency configuration implements this conditional logic?

A. Use two dependency chains: one that proceeds only when Task A succeeds and another that proceeds only when Task B has at least one failure; Task C depends on both resulting branches.
B. Configure Task C to depend on Task A and Task B with All done and use a notebook widget as the condition.
C. Task C depends on Task A with Success, with Task B listed as a negative dependency.
D. Create a Python task that checks dbutils.jobs.taskValues from Tasks A and B before running Task C.

Answer: A

NEW QUESTION # 154
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