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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
A data engineer is implementing a data ingestion pipeline that automatically detects schema changes in incoming JSON files and evolves the target Delta table accordingly. Which feature supports this automatic schema evolution?

A. Schema enforcement
B. Broadcast join
C. Schema evolution
D. Partition pruning

Answer: C

NEW QUESTION # 157
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can turn on the Auto Stop feature for the SQL endpoint.
B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
C. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
D. They can set up the dashboard's SQL endpoint to be serverless.
E. They can reduce the cluster size of the SQL endpoint.

Answer: A

Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity. This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as 240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. Reference: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard

NEW QUESTION # 158
A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several JSON files from containers on a storage account as soon as the file arrives within the storage account. Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

A. df = spark.readStream.format("cloudFiles").option("cloudFiles.format", "json").load("/input/path")
B. df = spark.readStream.format("json".load("input/path")
C. df = spark.read.json("input/path")
D. df = spark.readStream.format("cloud").option("json").load("/input/path")

Answer: A

Explanation:
To automatically ingest JSON files as soon as they arrive in cloud storage, the correct syntax is to use Auto Loader with spark.readStream.format("cloudFiles").option("cloudFiles.format",
"json").load("/input/path"). This enables continuous ingestion of new files into a streaming dataframe.

NEW QUESTION # 159
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

A. TBLPROPERTIES PII
B. "COMMENT PII"
C. There is no way to indicate whether a table contains PII.
D. PII
E. COMMENT "Contains PII"

Answer: E

Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

NEW QUESTION # 160
A data engineering team is using Kafka to capture event data and then ingest it into Databricks. The team wants to be able to see these historical events. Medallion architecture is already in place. The team wants to be mindful of costs.
Where should this historical event data be stored?

A. Gold
B. Bronze
C. Raw layer
D. Silver

Answer: B

NEW QUESTION # 161
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