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

Section	Weight	Objectives
Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. Aggregations, joins, and window functions 2. Complex data processing logic 3. DataFrame operations and transformations
Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - 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
Development and Ingestion	30%	- Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Delta Lake basics and usage 3. Connecting to external data sources - Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL
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
Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Data governance model 2. Permissions and access control

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
Which of the following commands will return the number of null values in the member_id column?

A. SELECT count(member_id) - count_null(member_id) FROM my_table;
B. SELECT null(member_id) FROM my_table;
C. SELECT count_if(member_id IS NULL) FROM my_table;
D. SELECT count_null(member_id) FROM my_table;
E. SELECT count(member_id) FROM my_table;

Answer: C

Explanation:
Explanation
https://docs.databricks.com/en/sql/language-manual/functions/count.html Returns A BIGINT.
If * is specified also counts row containing NULL values.
If expr are specified counts only rows for which all expr are not NULL.
If DISTINCT duplicate rows are not counted.

NEW QUESTION # 70
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. O They can reduce the cluster size of the SQL endpoint.
B. O They can set up the dashboard's SQL endpoint to be serverless.
C. Q They can turn on the Auto Stop feature for the SQL endpoint.
D. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: C

Explanation:
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.
References:Databricks documentation on SQL endpoints: SQL Endpoints in Databricks

NEW QUESTION # 71
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

Answer: E

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 # 72
After a failed multi-task job run, a data engineer changes the cluster size and updates a notebook path for one task. The data engineer wants to re-run just the failed/skipped tasks with the updated settings and keep the run lineage intact. Which strategy should the data engineer use to preserve the run history?

A. Use 'Run now' to start a new run, so that the updated settings apply; the new run replaces the prior run history.
B. Increase the task retry count and click 'Retry' on the failed task to automatically apply new cluster settings within the same run.
C. Use Jobs REST API repairRun to re-run failed tasks; the repair uses the current job and task settings and appears in the run history.
D. Export the job as a Databricks Asset Bundle, redeploy it, and re-run only the modified tasks from the bundle.

Answer: C

Explanation:
The repairRun capability allows rerunning only failed or skipped tasks while preserving the original run lineage, and it applies the current job and task settings during the repair execution.

NEW QUESTION # 73
A data engineer needs to ingest JSON change data from Salesforce into Unity Catalog-governed Delta tables with a low-code, fully managed experience. Which Databricks capability should the data engineer use?

A. Use the Salesforce REST API in a PySpark notebook to query changed records and write them to Delta tables
B. Auto Loader reading JSON files from an S3 bucket exported by Salesforce
C. Auto Loader with a file arrival trigger on DBFS
D. A Lakeflow Connect managed Salesforce connector that writes into Unity Catalog Delta tables

Answer: D

Explanation:
Lakeflow Connect's managed Salesforce connector provides a low-code, fully managed ingestion pipeline that incrementally loads Salesforce data into Unity Catalog-governed Delta tables.

NEW QUESTION # 74
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