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

Section	Objectives
Topic 1: Databricks Lakehouse Platform Fundamentals	- Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage
Topic 2: Data Processing and Transformations	- User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization)
Topic 3: Productionizing Data Pipelines	- Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization
Topic 4: Data Ingestion and ELT Development	- Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations
Topic 5: Data Governance and Quality	- Data access control and governance - Unity Catalog basics - Data quality concepts and management

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q196-Q201):

NEW QUESTION # 196
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 clone the existing task in the existing Job and update it to run the new notebook.
B. They can create a new job from scratch and add both tasks to run concurrently.
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 create a new task in the existing Job and then add it as a dependency of the original task.

Answer: D

NEW QUESTION # 197
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?

A. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
B. They can schedule the query to run every 1 day from the Jobs UI.
C. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
D. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
E. They can schedule the query to run every 12 hours from the Jobs UI.

Answer: A

Explanation:
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone.
This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
* In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
* Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
* Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
* Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. References: Schedule a query, What are Databricks SQL alerts?, Jobs.

NEW QUESTION # 198
A data engineer is setting up access control in Unity Catalog and needs to ensure that a group of data analysts can query tables but not modify data.
Which permission should the data engineer grant to the data analysts?

A. ALL PRIVILEGES
B. INSERT
C. SELECT
D. MODIFY

Answer: C

NEW QUESTION # 199
Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

A. Scheduled Workflows run continuously until manually stopped.
B. Scheduled Workflows process data as it arrives at configured sources.
C. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.
D. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.

Answer: C

Explanation:
Scheduled Workflows in Databricks are designed to trigger jobs at specified intervals. These workflows can be configured to start a cluster when the job is scheduled, and the cluster will only run for the duration of the task. Once the workflow completes, the cluster can be terminated, which helps to reduce resource consumption and costs since you are only paying for the cluster while the workflow is running.

NEW QUESTION # 200
A data engineer has manually created several Databricks jobs and dashboards using the workspace UI. The team now wants to manage these resources as code using Declarative Automation Bundles, formerly known as Databricks Asset Bundles, store the configuration in a Git repository, and deploy changes through CI/CD.
Which approach converts the existing resources into a bundle project?

A. Run databricks bundle generate for each existing resource, then use databricks bundle deployment bind to link the generated configuration to the corresponding existing workspace resource, and commit the bundle project to Git for CI/CD deployment.
B. Clone an empty Git repository into a Git folder, manually recreate each job and dashboard configuration in YAML by referencing the workspace UI, and then deploy with databricks bundle deploy.
C. Use the Databricks REST API to export each job's JSON definition, save the JSON files into a Git repository, and configure CI/CD to import them using the REST API during each deployment.
D. Run databricks bundle init to create a new bundle project, and then use databricks bundle deploy to automatically detect and import all existing workspace resources.

Answer: A

NEW QUESTION # 201
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