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

Section	Objectives
Topic 1: Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data - ETL patterns and transformations
Topic 2: Data Governance and Quality	- Unity Catalog basics - Data quality concepts and management - Data access control and governance
Topic 3: Data Processing and Transformations	- Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization) - PySpark DataFrame transformations - User-defined functions (UDFs)
Topic 4: Productionizing Data Pipelines	- Scheduling and monitoring jobs - Pipeline deployment and operationalization - Databricks Workflows / Jobs orchestration
Topic 5: Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q202-Q207):

NEW QUESTION # 202
A data provider must share a live Delta table with an external partner who uses an open-source Spark environment outside Databricks, without copying the data.
Which feature should be used?

A. A Unity Catalog external location with a storage credential granted to the partner
B. A materialized view exported as Parquet and delivered by SFTP
C. A deep clone written to a partner-owned S3 bucket nightly
D. Delta Sharing with a recipient and share

Answer: D

Explanation:
Delta Sharing is an open protocol that lets a provider share live data from Unity Catalog with recipients on any platform that supports the protocol, with no data replication. Access is governed by shares and recipients, and the recipient always reads the current version of the data.

NEW QUESTION # 203
A data engineer needs to conduct Exploratory Analysis on data residing in a database that is within the company's custom-defined network in the cloud. The data engineer is using SQL for this task.
Which type of SQL Warehouse will enable the data engineer to process large numbers of queries quickly and cost-effectively?

A. All-purpose compute cluster
B. Pro SQL Warehouse
C. Serverless SQL Warehouse
D. Classic SQL Warehouse

Answer: C

Explanation:
A serverless SQL Warehouse automatically scales to handle large numbers of queries, optimizes performance without manual configuration, and charges only for actual usage, making it the most cost-effective and efficient option for exploratory SQL analysis.

NEW QUESTION # 204
A data engineer is decommissioning a sandbox schema in Unity Catalog. Some tables are ephemeral staging outputs that can be safety removed entirely, but a few tables point at shared cloud storage used by downstream jobs outside Databricks. The engineer must avoid deleting any shared files when cleaning up catalog objects.
How does Unity Catalog behave when dropping Managed vs External tables?

A. DROP managed staging tables to remove data and metadata, and DROP external tables to remove only metadata
B. DROP managed tables that are ephemeral and DROP external tables; files for both remain for 7 days.
C. DROP external tables first to delete their files, then DROP managed tables to keep their files for recovery.
D. DROP all tables; Databricks will only remove metadata for both managed and external tables.

Answer: A

Explanation:
In Unity Catalog, dropping a managed table deletes both the table metadata and the underlying data files, while dropping an external table removes only the catalog metadata and leaves the referenced cloud storage data intact.

NEW QUESTION # 205
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A. A data lakehouse provides storage solutions for structured and unstructured data.
B. A data lakehouse allows the use of SQL queries to examine data.
C. A data lakehouse stores data in open formats.
D. A data lakehouse enables machine learning and artificial Intelligence workloads.
E. A data lakehouse supports ACID-compliant transactions.

Answer: E

Explanation:
Explanation
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality, especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.

NEW QUESTION # 206
A data engineer maintains a Declarative Automation Bundle (formerly Databricks Asset Bundle), which deploys a dashboard.

Which set of actions will enable the dashboard to be deployed in prod using catalog_prod without changing the resource definition?

A. Run databricks bundle deploy --var dataset_catalog=catalog_prod so the CLI selects targets.prod and applies dataset_catalog: catalog_prod automatically.
B. Run databricks bundle deploy --target prod so the deployment uses targets.prod and overrides dataset_catalog to catalog_prod.
C. Run databricks bundle execute --profile prod so the CLI selects targets.prod and applies dataset_catalog: catalog_prod automatically.
D. Run databricks bundle deploy --profile prod so the CLI selects targets.prod and applies dataset_catalog: catalog_prod automatically.

Answer: B

Explanation:
databricks bundle deploy --target prod selects the prod target, which overrides dataset_catalog with catalog_prod. A CLI profile controls authentication, not bundle target selection.

NEW QUESTION # 207
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