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

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

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Unique Databricks Databricks-Certified-Data-Engineer-Associate Pdf Questions

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q118-Q123):

NEW QUESTION # 118
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions. In which location can the data engineer review their permissions on the table?

A. Catalog Explorer
B. Dashboards
C. Jobs
D. Repos

Answer: A

Explanation:
https://docs.databricks.com/en/delta-live-tables/tutorial-pipelines.html

NEW QUESTION # 119
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to successfully complete before the new task begins
B. When another task needs to fail before the new task begins
C. When another task needs to be replaced by the new task
D. When another task has the same dependency libraries as the new task
E. When another task needs to use as little compute resources as possible

Answer: A

NEW QUESTION # 120
Which of the following tools is used by Auto Loader process data incrementally?

A. Data Explorer
B. Databricks SQL
C. Unity Catalog
D. Checkpointing
E. Spark Structured Streaming

Answer: E

NEW QUESTION # 121
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

A. Simplified governance
B. Cloud-specific integrations
C. Ability to scale workloads
D. Avoiding vendor lock-in
E. Ability to scale storage

Answer: D

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the Databricks Lakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). Reference: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.

NEW QUESTION # 122
Which query is performing a streaming hop from raw data to a Bronze table?

Answer: C

Explanation:
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:
Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.

NEW QUESTION # 123
......

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