Databricks-Certified-Data-Engineer-Associate New Guide Files, Databricks-Certified-Data-Engineer-Associate Latest Test Questions

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

SectionWeightObjectives
Development and Ingestion30%- Notebook development fundamentals
  • 1. Using PySpark and Spark SQL
  • 2. Data exploration and validation
- Data ingestion patterns and methods
  • 1. Connecting to external data sources
  • 2. Batch and streaming ingestion
  • 3. Delta Lake basics and usage
Data Processing & Transformations31%- Data transformation techniques
  • 1. DataFrame operations and transformations
  • 2. Aggregations, joins, and window functions
  • 3. Complex data processing logic
- Query optimization and performance
  • 1. Optimization strategies
  • 2. Understanding query plans
Productionizing Data Pipelines18%- Deployment and CI/CD
  • 1. Databricks Asset Bundles
  • 2. Version control integration
- Monitoring and troubleshooting
  • 1. Logging and error handling
  • 2. Pipeline reliability and recovery
- Workflow orchestration
  • 1. Scheduling, triggers, and dependencies
  • 2. Lakeflow Jobs creation and management
Data Governance & Quality11%- Unity Catalog implementation
  • 1. Data governance model
  • 2. Permissions and access control
- Data quality and reliability
  • 1. Data validation and quality checks
  • 2. Schema enforcement and evolution
Databricks Intelligence Platform10%- Platform architecture and core concepts
  • 1. Workspace navigation and management
  • 2. Data layout and optimization: partitioning, file sizing, caching
  • 3. Compute options: clusters, SQL warehouses, serverless

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q221-Q226):

NEW QUESTION # 221
A data engineer has inherited a Databricks pipeline from a previous team. The pipeline is missing its SLAs, and an initial investigation has identified memory spills in Spark. These increased runtimes are also driving up costs. The data engineer needs to reduce the runtime without significantly increasing costs. What should the data engineer do first to address this issue?

Answer: D

Explanation:
Memory spills in Spark often occur due to inefficient shuffling. The first step is to adjust the spark.sql.shuffle.partitions configuration, which controls the number of shuffle partitions.
Optimizing this setting reduces shuffle overhead and memory spills, improving performance without significantly increasing costs.


NEW QUESTION # 222
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

Answer: E

Explanation:
Explanation
A Single Node cluster is a cluster consisting of an Apache Spark driver and no Spark workers. A Single Node cluster supports Spark jobs and all Spark data sources, including Delta Lake. A Standard cluster requires a minimum of one Spark worker to run Spark jobs.


NEW QUESTION # 223
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?

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 toindicate 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 # 224
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?

Answer: D

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 # 225
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?

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 # 226
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