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

SectionWeightObjectives
Topic 1: Databricks Intelligence Platform10%- Platform architecture and core concepts
  • 1. Data layout and optimization: partitioning, file sizing, caching
  • 2. Compute options: clusters, SQL warehouses, serverless
  • 3. Workspace navigation and management
Topic 2: Productionizing Data Pipelines18%- Workflow orchestration
  • 1. Lakeflow Jobs creation and management
  • 2. Scheduling, triggers, and dependencies
- Deployment and CI/CD
  • 1. Databricks Asset Bundles
  • 2. Version control integration
- Monitoring and troubleshooting
  • 1. Logging and error handling
  • 2. Pipeline reliability and recovery
Topic 3: Data Governance & Quality11%- Data quality and reliability
  • 1. Schema enforcement and evolution
  • 2. Data validation and quality checks
- Unity Catalog implementation
  • 1. Permissions and access control
  • 2. Data governance model
Topic 4: Data Processing & Transformations31%- Data transformation techniques
  • 1. Aggregations, joins, and window functions
  • 2. Complex data processing logic
  • 3. DataFrame operations and transformations
- Query optimization and performance
  • 1. Understanding query plans
  • 2. Optimization strategies
Topic 5: Development and Ingestion30%- Data ingestion patterns and methods
  • 1. Connecting to external data sources
  • 2. Delta Lake basics and usage
  • 3. Batch and streaming ingestion
- Notebook development fundamentals
  • 1. Using PySpark and Spark SQL
  • 2. Data exploration and validation

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q233-Q238):

NEW QUESTION # 233
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: B

Explanation:
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.


NEW QUESTION # 234
Which of the following describes the type of workloads that are always compatible with Auto Loader?

Answer: C

Explanation:
Auto Loader is a Structured Streaming source that incrementally and efficiently processes new data files as they arrive in cloud storage. It supports both Python and SQL in Delta Live Tables, which are ideal for building streaming data pipelines. Auto Loader can handle near real-time ingestion of millions of files per hour and provide exactly-once guarantees when writing data into Delta Lake. Auto Loader is not designed for dashboard, machine learning, serverless, or batch workloads, which have different requirements and characteristics. Reference: What is Auto Loader?, Delta Live Tables


NEW QUESTION # 235
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

Answer: A

Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches
100. To set up a webhook alert destination, the data engineer needs to do the following steps:
* In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
* Click Alert Destinations and click Add New Alert Destination.
* Select Webhook and enter the webhook URL and the optional custom template for the notification message.
* Click Create to save the webhook alert destination.
* In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
* Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
* In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.


NEW QUESTION # 236
A data engineer wants to create a reproducible ETL workflow that runs every night and executes multiple notebooks in sequence. The workflow should retry failed tasks and provide monitoring capabilities. Which Databricks feature is best suited for this requirement?

Answer: A


NEW QUESTION # 237
A data engineer is maintaining an ETL pipeline code with a GitHub repository linked to their Databricks account. The data engineer wants to deploy the ETL pipeline to production as a databricks workflow. Which approach should the data engineer use?

Answer: B

Explanation:
The best practice for deploying ETL pipelines to production is to use Databricks Asset Bundles (DAB) with GitHub integration. DAB provides a declarative YAML-based structure for defining workflows and resources, supports CI/CD, and integrates seamlessly with GitHub, enabling version-controlled, automated deployments to production.


NEW QUESTION # 238
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