Databricks-Certified-Data-Engineer-Associate Exam Material - Databricks-Certified-Data-Engineer-Associate Exam Registration

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A data engineer is setting up access control in Unity Catalog and needs to ensure that a group of data analysts can query tables but not modify data. Which permission should the data engineer grant to the data analysts?

A. ALL PRIVILEGES
B. SELECT
C. INSERT
D. MODIFY

Answer: B

Explanation:
Granting the SELECT permission in Unity Catalog allows data analysts to query and read data from tables without being able to modify, insert, or delete records. This enforces read-only access, which matches the requirement.

NEW QUESTION # 57
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(continuous="5 seconds")
B. trigger(once="5 seconds")
C. trigger("5 seconds")
D. trigger(processingTime="5 seconds")
E. trigger()

Answer: D

NEW QUESTION # 58
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

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

A. org.apache.spark.sql.sqlite
B. autoloader
C. sqlite
D. DELTA
E. org.apache.spark.sql.jdbc

Answer: C

Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver

NEW QUESTION # 59
Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A. A data lakehouse allows the use of SQL queries to examine data.
B. A data lakehouse enables machine learning and artificial Intelligence workloads.
C. A data lakehouse stores data in open formats.
D. A data lakehouse supports ACID-compliant transactions.
E. A data lakehouse provides storage solutions for structured and unstructured data.

Answer: D

Explanation:
A data lakehouse is a data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data12. One of the key features of a data lakehouse is that it supports ACID-compliant transactions, which means that it ensures data integrity, consistency, and isolation across concurrent read and write operations3. This feature results in improved data quality over a traditional data lake, which does not support transactions and may suffer from data corruption, duplication, or inconsistency due to concurrent or streaming data ingestion and processing . References: 1: What is a Data Lakehouse? - Databricks 2: What is a Data Lakehouse? Definition, features & benefits. - Qlik 3: ACID Transactions - Databricks : [Data Lake vs Data Warehouse: Key Differences] : [Data Lakehouse: The Future of Data Engineering]

NEW QUESTION # 60
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
B. Replace predict with a stream-friendly prediction function
C. Replace "transactions" with the path to the location of the Delta table
D. Replace spark.read with spark.readStream
E. Replace format("delta") with format("stream")

Answer: D

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 61
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