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

Section	Objectives
Topic 1: Data Governance and Quality	- Data access control and governance - Data quality concepts and management - Unity Catalog basics
Topic 2: Data Ingestion and ELT Development	- ETL patterns and transformations - Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data
Topic 3: Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs
Topic 4: Data Processing and Transformations	- User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) - Delta Lake fundamentals (tables, transactions, optimization) - PySpark DataFrame transformations
Topic 5: Databricks Lakehouse Platform Fundamentals	- Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q294-Q299):

NEW QUESTION # 294
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="5 seconds")
B. trigger(once="5 seconds")
C. trigger("5 seconds")
D. trigger(continuous="5 seconds")
E. trigger()

Answer: A

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 295
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. All of the fields had at least one null value
B. Auto Loader only works with string data
C. There was a type mismatch between the specific schema and the inferred schema
D. Auto Loader cannot infer the schema of ingested data
E. JSON data is a text-based format

Answer: E

NEW QUESTION # 296
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. USING DELTA
B. FROM CSV
C. USING CSV
D. FROM "path/to/csv"
E. None of these lines of code are needed to successfully complete the task

Answer: C

NEW QUESTION # 297
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

Answer: B

Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html

NEW QUESTION # 298
Identify a scenario to use an external table.
A Data Engineer needs to create a parquet bronze table and wants to ensure that it gets stored in a specific path in an external location.
Which table can be created in this scenario?

A. A managed table where the catalog has managed location pointing to specific path in external location.
B. An external table where the location is pointing to specific path in external location.
C. An external table where the schema has managed location pointing to specific path in external location.
D. A managed table where the location is pointing to specific path in external location.

Answer: B

Explanation:
In this scenario, the Data Engineer wants to create a parquet bronze table and ensure that it gets stored in a specific path in an external location. An external table allows you to define the table's structure while storing the actual data in an external location, which can be beneficial for data sharing and managing storage separately from the data warehouse catalog.
An external table where the schema has managed location pointing to specific path in external location: This is incorrect because an external table does not have a managed location; its data is stored in an external location specified by the user.
A managed table where the catalog has managed location pointing to specific path in external location: This is incorrect as managed tables typically store their data in the default location of the database unless specified otherwise, which does not fit the requirement for external storage.
A managed table where the location is pointing to specific path in external location: This is incorrect because managed tables do not allow you to specify an external location; they manage their own data storage.

NEW QUESTION # 299
......

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