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

Section	Weight	Objectives
Topic 1: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits
Topic 2: Apache Spark Data Processing Fundamentals	20-25%	- Use Spark SQL for data processing - Apply transformations and actions on DataFrames - Create and use Spark DataFrames - Work with structured data types (arrays, maps, structs)
Topic 3: Delta Lake Fundamentals	20-25%	- Write to and read from Delta tables - Understand ACID transactions and time travel - Create and manage Delta tables - Explain Delta Lake features and benefits
Topic 4: Data Pipeline Architecture	15-20%	- Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Implement incremental data processing - Monitor and optimize pipeline performance
Topic 5: Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Implement user-defined functions (UDFs) - Use PySpark for data processing
Topic 6: Spark SQL and DataFrames	15-20%	- Handle null values and data quality - Join and union DataFrames - Aggregate and group data - Write and execute Spark SQL queries

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q155-Q160):

NEW QUESTION # 155
Which file format is used for storing Delta Lake Table?

A. SV
B. JSON
C. Parquet
D. Delta

Answer: C

NEW QUESTION # 156
Which two conditions are applicable for governance in Databricks Unity Catalog? (Choose two.)

A. If metastore is not associated with location, it's mandatory to associate catalog with managed locations
B. You can have multiple catalogs within metastore and 1 catalog can be associated with multiple metastore
C. You can have more than 1 metastore within a databricks account console but only 1 per region.
D. If catalog is not associated with location, it's mandatory to associate schema with managed locations
E. Both catalog and schema must have a managed location in Unity Catalog provided metastore is not associated with a location

Answer: A,C

Explanation:
You can have more than 1 metastore within a Databricks account console but only 1 per region:
This is correct. In Databricks Unity Catalog, you can create multiple metastores, but you can only have one metastore per region within an account.
If metastore is not associated with location, it's mandatory to associate catalog with managed locations: This is also correct. If a metastore does not have a default storage location, then the catalog must be associated with a managed location to ensure that data is stored properly within Unity Catalog.

NEW QUESTION # 157
Which of the following benefits is provided by the array functions from Spark SQL?

A. An ability to work with complex, nested data ingested from JSON files
B. An ability to work with an array of tables for procedural automation
C. An ability to work with data within certain partitions and windows
D. An ability to work with data in a variety of types at once
E. An ability to work with time-related data in specified intervals

Answer: A

Explanation:
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

NEW QUESTION # 158
A data engineer is onboarding a new Bronze ingestion pipeline in Databricks with Unity Catalog. The team wants Databricks to handle storage layout, apply platform optimizations over time, and simplify lifecycle management so that when a table is dropped, its underlying data is also cleaned up according to Databricks- managed retention policies.
Which table type should the data engineer create for these ingestion tables?

A. External tables with a LOCATION pointing to an external volume for full control of file layout
B. Managed tables so that Unity Catalog manages both metadata and underlying data lifecycle
C. Foreign tables federated from an external catalog to delegate optimization to the source system
D. Temporary views over files to avoid table-level governance and lifecycle coupling

Answer: B

NEW QUESTION # 159
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 requires manual synchronization of results from the cluster
B. It runs all Spark compute locally and only reads remote files
C. It proxies Spark commands from the local client to the remote cluster
D. It mirrors the entire cluster environment on the developer's machine

Answer: C

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 # 160
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