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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q287-Q292):

NEW QUESTION # 287
A data engineer must apply a daily batch of customer records to a Delta table so that existing customers are updated, new customers are inserted, and no duplicates are created.
Which statement accomplishes this in a single atomic operation?

A. MERGE INTO customers t USING updates s ON t.customer_id = s.customer_id WHEN MATCHED THEN UPDATE SET * WHEN NOT MATCHED THEN INSERT *
B. INSERT OVERWRITE TABLE customers SELECT * FROM updates
C. INSERT INTO customers SELECT * FROM updates
D. CREATE OR REPLACE TABLE customers AS SELECT * FROM updates

Answer: A

Explanation:
MERGE INTO performs upserts in a single ACID transaction, updating matched rows and inserting unmatched rows. INSERT INTO would create duplicates, and both INSERT OVERWRITE and CREATE OR REPLACE would discard existing records not present in the daily batch.

NEW QUESTION # 288
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

A. Avoiding vendor lock-in
B. Ability to scale workloads
C. Ability to scale storage
D. Simplified governance
E. Cloud-specific integrations

Answer: A

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark™, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the DatabricksLakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.

NEW QUESTION # 289
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
B. They can set up the dashboard's SQL endpoint to be serverless.
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can reduce the cluster size of the SQL endpoint.
E. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: B

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. References:
* Create a serverless SQL endpoint
* Assign a SQL endpoint to a dashboard
* SQL Analytics API

NEW QUESTION # 290
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 incrementally.
B. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
C. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
D. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
E. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.

Answer: A

Explanation:
A streaming live table or view processes data that has been added only since the last pipeline update.
Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

NEW QUESTION # 291
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="5 seconds")
B. trigger(once="5 seconds")
C. trigger()
D. trigger("5 seconds")
E. trigger(continuous="5 seconds")

Answer: A

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 292
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