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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q284-Q289):

NEW QUESTION # 284
Which of the following must be specified when creating a new Delta Live Tables pipeline?

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

Answer: E

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.
Tutorial: Run your first Delta Live Tables pipeline, What is Delta Live Tables?, Create a pipeline, Pipeline configuration.

NEW QUESTION # 285
A data engineer investigates a slow stage in the Databricks Spark UI. The engineer scrolls down to the Summary Metrics section for task durations and observes the following metrics for that stage:
25th-percentile duration: 40 seconds
Median duration: 45 seconds
75th-percentile duration: 50 seconds
Maximum duration: 80 seconds
What is the correct interpretation of these metrics?

A. The stage is healthy.
B. There is a garbage-collection issue.
C. The cluster is under-provisioned.
D. The stage likely suffers from data skew.

Answer: D

NEW QUESTION # 286
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 Data Explorer
B. Review the Permissions tab in the table's page in Data Explorer
C. Review the Owner field in the table's page in the cloud storage solution
D. There is no way to identify the owner of the table
E. All of these options can be used to identify the owner of the table

Answer: A

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.
References:
* 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 # 287
A team is designing their Data bricks Asset Bundle for deployment. They want to ensure their bundle includes all necessary resources, targeted environment configurations, and deployment settings in a single version-controlled file. What is required as the main configuration file at the root of their project?

A. A JSON file named bundle_config.json that stores all resource details and environment variables
B. A databricks.yml YAML file that contains the bundle's name, targets, includes resources, and variables
C. A directory with multiple TXT files, each describing a single resource for the bundle
D. A Python script called configure_bundle.py that dynamically creates resources at runtime

Answer: B

Explanation:
Databricks Asset Bundles require a databricks.yml file at the project root, which defines the bundle configuration including targets, resources, variables, and deployment settings in a single version-controlled specification.

NEW QUESTION # 288
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation cause the job to fail.
B. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
E. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.

Answer: D

NEW QUESTION # 289
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