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

Section	Weight	Objectives
Topic 1: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Connecting to external data sources 3. Delta Lake basics and usage - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation
Topic 2: Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Permissions and access control 2. Data governance model
Topic 3: Data Processing & Transformations	31%	- Data transformation techniques 1. DataFrame operations and transformations 2. Complex data processing logic 3. Aggregations, joins, and window functions - Query optimization and performance 1. Understanding query plans 2. Optimization strategies
Topic 4: 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. Databricks Asset Bundles 2. Version control integration
Topic 5: Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Workspace navigation and management 3. Compute options: clusters, SQL warehouses, serverless

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q192-Q197):

NEW QUESTION # 192
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

A. There is no way to indicate whether a table contains PII.
B. "COMMENT PII"
C. PII
D. TBLPROPERTIES PII
E. COMMENT "Contains PII"

Answer: E

NEW QUESTION # 193
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

A. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
B. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
C. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
D. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
E. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.

Answer: C

Explanation:
Explanation
The CREATE STREAMING LIVE TABLE syntax is used when you want to create Delta Live Tables (DLT) tables that are designed for processing data incrementally. This is typically used when your data pipeline involves streaming or incremental data updates, and you want the table to stay up to date as new data arrives.
It allows you to define tables that can handle data changes incrementally without the need for full table refreshes.

NEW QUESTION # 194
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 without notifications.
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 with a new email alert destination.

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 . Reference: Databricks Documentation - Manage notification destinations, Databricks Documentation - Create alerts for Databricks SQL queries, Databricks Documentation - Configure alert conditions and messages.

NEW QUESTION # 195
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain aggregations than Silver tables.
B. Gold tables are more likely to contain a less refined view of data than Silver tables.
C. Gold tables are more likely to contain truthful data than Silver tables.
D. Gold tables are more likely to contain more data than Silver tables.
E. Gold tables are more likely to contain valuable data than Silver tables.

Answer: A

Explanation:
Explanation
In some data processing pipelines, especially those following a typical "Bronze-Silver-Gold" data lakehouse architecture, Silver tables are often considered a more refined version of the raw or Bronze data. Silver tables may include data cleansing, schema enforcement, and some initial transformations. Gold tables, on the other hand, typically represent a stage where data is further enriched, aggregated, and processed to provide valuable insights for analytical purposes. This could indeed involve more aggregations compared to Silver tables.

NEW QUESTION # 196
A data engineering team wants to validate a new ingestion pipeline locally while ensuring large aggregations run in serverless compute in their Databricks workspace. They plan to use Databricks Connect and have the option to attach to either a shared cluster or serverless.
Which workspace requirement should be confirmed first to avoid connection failures?

A. Verify that only assigned access mode clusters are used because serverless is not supported by Databricks Connect.
B. Verify that the workspace has Unity Catalog disabled and the Databricks Connect version is less than the serverless Runtime version.
C. Verify that the local Spark version equals the serverless Spark version to satisfy Spark Connect parity.
D. Verify that the workspace has Unity Catalog enabled and that the Databricks Connect version supports serverless for the target Runtime release.

Answer: D

Explanation:
Databricks Connect requires Unity Catalog to be enabled when connecting to serverless compute, and the Databricks Connect version must explicitly support serverless for the target Databricks Runtime to ensure compatibility and prevent connection failures.

NEW QUESTION # 197
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