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Databricks Certified Data Engineer Associate Exam is a vendor-neutral certification program, which means that it is not tied to any specific technology or vendor. This makes it an ideal certification program for individuals who want to demonstrate their expertise in data engineering using Databricks, regardless of the technology or vendor they work with.

One of the unique features of the Databricks Certified Data Engineer Associate certification is that it is focused on Databricks, a cloud-based big data processing platform. Databricks is becoming increasingly popular among data professionals, and the certification is designed to test individuals' knowledge of this platform. This makes the certification especially valuable for individuals who work with Databricks or plan to work with it in the future.

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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed to validate the skills and knowledge of data engineers who work with the Databricks Unified Analytics Platform. Databricks Certified Data Engineer Associate Exam certification is ideal for professionals who want to demonstrate their expertise in building and optimizing data pipelines, data transformation, and data storage using Databricks.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.

Answer: E

NEW QUESTION # 75
A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths.
How should the team use databricks.yml to satisfy these requirements?

A. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
B. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
C. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
D. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default: true and override workspace paths or artifacts under that target.

Answer: D

Explanation:
In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.
Reference Source: Databricks Asset Bundle Configuration Guide - "Structure of databricks.yml and target overrides."

NEW QUESTION # 76
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

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

Answer: E

Explanation:
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. Reference: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation

NEW QUESTION # 77
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?

A. Database
B. View
C. Function
D. Temporary view
E. Table

Answer: E

Explanation:
A table is a data entity that is stored in a physical location and can be accessed by other data engineers in other sessions. A table can be created from one or more tables using the CREATE TABLE or CREATE TABLE AS SELECT commands. A table can also be registered from an existing DataFrame using the spark.catalog.createTable method. A table can be queried using SQL or DataFrame APIs. A table can also be updated, deleted, or appended using the MERGE INTO command or the DeltaTable API. Reference:
Create a table
Create a table from a query result
Register a table from a DataFrame
[Query a table]
[Update, delete, or merge into a table]

NEW QUESTION # 78
A data engineering team is designing the Gold layer in their Unity Catalog-governed lakehouse for downstream BI and analytics users. The team wants to expose business-ready metrics with fast query performance and consistent definitions, while keeping the transformation logic in Spark notebooks. Which type of Gold layer object meets this requirement?

A. A materialized view that precomputes aggregations from Silver tables on a schedule and is queried by BI tools for faster, consistent analytics.
B. A streaming table that continuously ingests raw events from source systems and exposes them directly to BI users.
C. A view that directly references raw Bronze tables so BI tools can define their own joins and aggregations.
D. A collection of CSV files stored in a cloud storage object. BI users will parse the files to query the gold layer data.

Answer: A

Explanation:
A materialized view stores precomputed Gold-layer aggregations, providing BI tools with fast queries and centrally defined, consistent business results while allowing the transformation logic to remain in Databricks notebooks.

NEW QUESTION # 79
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