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

Section	Weight	Objectives
Topic 1: 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 2: Development and Ingestion	30%	- Data ingestion patterns and methods 1. Connecting to external data sources 2. Delta Lake basics and usage 3. Batch and streaming ingestion - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation
Topic 3: Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Version control integration 2. Databricks Asset Bundles - 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
Topic 4: Data Processing & Transformations	31%	- Data transformation techniques 1. Aggregations, joins, and window functions 2. DataFrame operations and transformations 3. Complex data processing logic - Query optimization and performance 1. Optimization strategies 2. Understanding query plans
Topic 5: 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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
Which of the following must be specified when creating a new Delta Live Tables pipeline?

A. The preferred DBU/hour cost
B. At least one notebook library to be executed
C. A key-value pair configuration
D. A location of a target database for the written data
E. A path to cloud storage location for the written data

Answer: B

Explanation:
Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode.
Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.
References: Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.

NEW QUESTION # 44
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

A. Option A
B. Option E
C. Option B
D. Option C
E. Option D

Answer: B

Explanation:
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

NEW QUESTION # 45
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

A. Databricks SQL
B. Data Explorer
C. Delta Lake
D. Unity Catalog
E. Auto Loader

Answer: E

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage. Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. Reference: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 46
A data engineer is maintaining an ETL pipeline code with a GitHub repository linked to their Databricks account. The data engineer wants to deploy the ETL pipeline to production as a databricks workflow. Which approach should the data engineer use?

A. Databricks Asset Bundles (DAB) + GitHub Integration
B. Maintain workflow_config.json and deploy it using Databricks CLI
C. Manually create and manage the workflow in UI
D. Maintain workflow_config.json and deploy it using Terraform

Answer: A

Explanation:
The best practice for deploying ETL pipelines to production is to use Databricks Asset Bundles (DAB) with GitHub integration. DAB provides a declarative YAML-based structure for defining workflows and resources, supports CI/CD, and integrates seamlessly with GitHub, enabling version-controlled, automated deployments to production.

NEW QUESTION # 47
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

A. Auto Loader only works with string data
B. Auto Loader cannot infer the schema of ingested data
C. All of the fields had at least one null value
D. JSON data is a text-based format
E. There was a type mismatch between the specific schema and the inferred schema

Answer: D

NEW QUESTION # 48
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