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

Section	Weight	Objectives
Topic 1: Development and Ingestion	30%	- Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL - Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Connecting to external data sources 3. Delta Lake basics and usage
Topic 2: Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Pipeline reliability and recovery 2. Logging and error handling - Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management
Topic 3: Data Governance & Quality	11%	- Unity Catalog implementation 1. Data governance model 2. Permissions and access control - Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution
Topic 4: Databricks Intelligence Platform	10%	- 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 5: Data Processing & Transformations	31%	- 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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
A developer has configured Databricks Connect and starts running PySpark code locally. They notice that Data Frame operations are executed on the remote cluster, but actions like .show() return results directly in their local console. What does this behaviour illustrate about how Databricks Connect is used?

A. It proxies Spark commands from the local client to the remote cluster
B. It runs all Spark compute locally and only reads remote files
C. It requires manual synchronization of results from the cluster
D. It mirrors the entire cluster environment on the developer's machine

Answer: A

Explanation:
Databricks Connect forwards DataFrame transformations and actions from the local environment to the remote cluster for execution, while returning results such as .show() back to the local client, demonstrating a proxy-based interaction model.

NEW QUESTION # 117
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

A. Review the Owner field in the table's page in the cloud storage solution
B. There is no way to identify the owner of the table
C. Review the Owner field in the table's page in Data Explorer
D. Review the Permissions tab in the table's page in Data Explorer
E. All of these options can be used to identify the owner of the table

Answer: C

Explanation:
he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5. There is a way to identify the owner of the table, as explained above, so option E is false.
Reference:
1: Data Explorer | Databricks on AWS
2: Table details | Databricks on AWS
3: Set owner when creating a view in databricks sql - Databricks - 9978
4: Table access control | Databricks on AWS
5: External tables | Databricks on AWS

NEW QUESTION # 118
A data engineer is managing a data pipeline in Databricks, where multiple Delta tables are used for various transformations. The team wants to track how data flows through the pipeline, including identifying dependencies between Delta tables, notebooks, jobs, and dashboards. The data engineer is utilizing the Unity Catalog lineage feature to monitor this process.
How does Unity Catalog's data lineage feature support the visualization of relationships between Delta tables, notebooks, jobs, and dashboards?

A. Unity Catalog lineage provides an interactive graph that tracks dependencies between tables and notebooks but excludes any job-related dependencies or dashboard visualizations.
B. Unity Catalog lineage visualizes dependencies between Delta tables, notebooks, and jobs, but does not provide column-level tracing or relationships with dashboards.
C. Unity Catalog lineage only supports visualizing relationships at the table level and does not extend to notebooks, jobs, or dashboards.
D. Unity Catalog provides an interactive graph that visualizes the dependencies between Delta tables, notebooks, jobs, and dashboards, while also supporting column-level tracking of data transformations.

Answer: D

Explanation:
Databricks documentation for Unity Catalog lineage states that lineage is captured across queries run on Databricks , is supported for all languages , and is tracked down to the column level . It also explicitly says that lineage data includes related notebooks, jobs, and dashboards , and that the lineage can be visualized in Catalog Explorer in near real time as an interactive graph. This directly matches option D. The value of this feature is that it helps engineers understand where a table's data came from, which upstream notebooks or jobs produced it, and which downstream assets, including dashboards, depend on it. Column-level lineage adds another layer of traceability by showing how specific fields are derived through transformations, which is especially useful for impact analysis, governance, and troubleshooting. Options A, B, and C each remove documented capabilities that Unity Catalog lineage actually supports. Therefore, the Databricks-documented answer is the full interactive graph across Delta tables and related assets, with column-level tracking included.
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NEW QUESTION # 119
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 code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

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

Answer: D

NEW QUESTION # 120
A company uses Delta Sharing to collaborate with partners across different cloud providers and geographic regions. What will result in additional costs due to cross-region or egress fees?

A. Utilizing Delta Sharing for internal data analytics within a single cloud environment
B. Accessing Delta Sharing data using a VPN within the same data center
C. Transferring data via Delta Sharing across clouds and across different geographic regions
D. Sharing data within the same cloud provider and region

Answer: C

Explanation:
Transferring data via Delta Sharing across clouds and across different geographic regions incurs additional costs because cloud providers charge cross-region and egress fees when data leaves a region or moves between providers.

NEW QUESTION # 121
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