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

Section	Weight	Objectives
Development and Ingestion	30%	- Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Connecting to external data sources 3. Delta Lake basics and usage - Notebook development fundamentals 1. Using PySpark and Spark SQL 2. Data exploration and validation
Data Processing & Transformations	31%	- Data transformation techniques 1. Aggregations, joins, and window functions 2. DataFrame operations and transformations 3. Complex data processing logic - Query optimization and performance 1. Optimization strategies 2. Understanding query plans
Productionizing Data Pipelines	18%	- Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Workflow orchestration 1. Scheduling, triggers, and dependencies 2. Lakeflow Jobs creation and management - Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery
Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Compute options: clusters, SQL warehouses, serverless 2. Workspace navigation and management 3. Data layout and optimization: partitioning, file sizing, caching
Data Governance & Quality	11%	- Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution - Unity Catalog implementation 1. Data governance model 2. Permissions and access control

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q131-Q136):

NEW QUESTION # 131
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?

A. They can set up an Alert without notifications.
B. They can set up an Alert with one-time notifications.
C. They can set up an Alert with a new webhook alert destination.
D. They can set up an Alert with a custom template.
E. They can set up an Alert with a new email alert destination.

Answer: C

Explanation:
Explanation
To achieve this, the data engineer can set up an Alert in the Databricks workspace that triggers when the query results exceed the threshold of 100 NULL values. They can create a new webhook alert destination in the Alert's configuration settings and provide the necessary messaging webhook URL to receive notifications.
When the Alert is triggered, it will send a message to the configured webhook URL, which will then notify the entire team of the issue.

NEW QUESTION # 132
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 format("delta") with format("stream")
B. Replace "transactions" with the path to the location of the Delta table
C. Replace spark.read with spark.readStream
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace predict with a stream-friendly prediction function

Answer: C

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 # 133
A data engineer is cleaning a bronze table that receives the same customer records from multiple source systems. Duplicate rows have the same customer_id and email but different ingestion_timestamp values. The silver table should contain only one record per unique combination of customer_id and email. Which PySpark operation correctly deduplicates based on the business keys?

A. df.groupBy('customer_id', 'email').agg(max('ingestion_timestamp').alias('latest_ts'))
B. df.select('customer_id', 'email').distinct()
C. df.distinct()
D. df.dropDuplicates(['customer_id', 'email'])

Answer: D

Explanation:
dropDuplicates(['customer_id', 'email']) removes duplicates using only those business-key columns while retaining one complete row, including the other fields.

NEW QUESTION # 134
A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

A. All of these options can be used to identify the owner of the table
B. Review the Owner field in the table's page in the cloud storage solution
C. Review the Owner field in the table's page in Data Explorer
D. There is no way to identify the owner of the table
E. Review the Permissions tab in the table's page in Data Explorer

Answer: C

Explanation:
he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5.
There is a way to identify the owner of the table, as explained above, so option E is false.
References:
* 1: Data Explorer | Databricks on AWS
* 2: Table details | Databricks on AWS
* 3: Set owner when creating a view in databricks sql - Databricks - 9978
* 4: Table access control | Databricks on AWS
* 5: External tables | Databricks on AWS

NEW QUESTION # 135
A data engineering team runs multiple daily batch jobs. The team needs to run the jobs on a fixed overnight schedule with consistent performance and controlled resource usage. The team wants to minimize operational overhead and reduce idle cluster costs.
Which compute option should the team use?

A. Classic all-purpose compute
B. Serverless jobs compute
C. SQL warehouses
D. Classic jobs compute

Answer: B

NEW QUESTION # 136
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