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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q171-Q176):

NEW QUESTION # 171
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 cannot infer the schema of ingested data
B. All of the fields had at least one null value
C. There was a type mismatch between the specific schema and the inferred schema
D. JSON data is a text-based format
E. Auto Loader only works with string data

Answer: D

NEW QUESTION # 172
A data engineer is reading a very large dataset that contains skewed key values. Some partitions contain significantly more records than others, causing uneven workload distribution across executors. Which Spark issue does this scenario describe?

A. Schema evolution
B. Data skew
C. Broadcast failure
D. Lazy evaluation

Answer: B

NEW QUESTION # 173
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 location is pointing to specific path in external location.
B. An external table where the schema has managed location pointing to specific path in external location.
C. An external table where the location is pointing to specific path in external location.
D. A managed table where the catalog has managed location pointing to specific path in external location.

Answer: C

NEW QUESTION # 174
The Delta transaction log for the 'students' tables is shown using the 'DESCRIBE HISTORY students' command. A Data Engineer needs to query the table as it existed before the UPDATE operation listed in the log.

Which command should the Data Engineer use to achieve this? (Choose two.)

A. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:47.000+00:00'
B. SELECT * FROM students VERSION AS OF 5
C. SELECT * FROM students FROM HISTORY VERSION AS OF 3
D. SELECT * FROM students@v4
E. SELECT * FROM students TIMESTAMP AS OF '2024-04-22T 14:32:58.000+00:00'

Answer: A,D

NEW QUESTION # 175
Which of the following describes a scenario in which a data engineer will want to use a single- node cluster?

A. When they are manually running reports with a large amount of data
B. When they are running automated reports to be refreshed as quickly as possible
C. When they are working with SQL within Databricks SQL
D. When they are working interactively with a small amount of data
E. When they are concerned about the ability to automatically scale with larger data

Answer: D

NEW QUESTION # 176
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