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

Section	Weight	Objectives
Topic 1: 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
Topic 2: Data Governance & Quality	11%	- 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 3: Productionizing Data Pipelines	18%	- 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. Pipeline reliability and recovery 2. Logging and error handling
Topic 4: 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. Optimization strategies 2. Understanding query plans
Topic 5: 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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q179-Q184):

NEW QUESTION # 179
A data engineer manages multiple external tables linked to various data sources. The data engineer wants to manage these external tables efficiently and ensure that only the necessary permissions are granted to users for accessing specific external tables. How should the data engineer manage access to these external tables?

A. Create a single user role with full access to all external tables and assign it to all users.
B. Set up Azure Blob Storage permissions at the container level, allowing access to all external tables.
C. Use Unity Catalog to manage access controls and permissions for each external table individually.
D. Grant permissions on the Databricks workspace level, which will automatically apply to all external tables.

Answer: C

Explanation:
The correct approach is to use Unity Catalog to manage fine-grained access controls and permissions for each external table individually. Unity Catalog centralizes governance and ensures users only have access to the specific external tables they need, instead of granting broad storage or workspace-level permissions.

NEW QUESTION # 180
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing.
Which role must be in place to perform these actions across the metastore?

A. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
B. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
C. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.
D. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.

Answer: D

Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore. They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.
Reference Source: Databricks Unity Catalog Administration Guide - "Metastore admin privileges and ownership transfer."

NEW QUESTION # 181
A data engineering team is building a new data-transformation notebook. During development, the engineers need fast testing, quick code changes, and easy debugging. Later, the notebook will run nightly as a scheduled job without human intervention. The team wants to optimize for development speed.
Which compute should the team use during development?

A. An instance pool
B. Classic jobs compute
C. Interactive compute
D. A SQL warehouse

Answer: C

NEW QUESTION # 182
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean.
However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

A. spark.table
B. spark.sql
C. spark.delta.table
D. SELECT * FROM sales
E. There is no way to share data between PySpark and SQL.

Answer: B

NEW QUESTION # 183
A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several JSON files from containers on a storage account as soon as the file arrives within the storage account. Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

A. df = spark.readStream.format("json".load("input/path")
B. df = spark.readStream.format("cloudFiles").option("cloudFiles.format", "json").load("/input/path")
C. df = spark.readStream.format("cloud").option("json").load("/input/path")
D. df = spark.read.json("input/path")

Answer: B

Explanation:
To automatically ingest JSON files as soon as they arrive in cloud storage, the correct syntax is to use Auto Loader with spark.readStream.format("cloudFiles").option("cloudFiles.format",
"json").load("/input/path"). This enables continuous ingestion of new files into a streaming dataframe.

NEW QUESTION # 184
......

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