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

SectionWeightObjectives
Topic 1: Productionizing Data Pipelines18%- Deployment and CI/CD
  • 1. Version control integration
  • 2. Databricks Asset Bundles
- Monitoring and troubleshooting
  • 1. Pipeline reliability and recovery
  • 2. Logging and error handling
- Workflow orchestration
  • 1. Scheduling, triggers, and dependencies
  • 2. Lakeflow Jobs creation and management
Topic 2: Databricks Intelligence Platform10%- 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
Topic 3: Data Processing & Transformations31%- 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
Topic 4: Development and Ingestion30%- Notebook development fundamentals
  • 1. Data exploration and validation
  • 2. Using PySpark and Spark SQL
- Data ingestion patterns and methods
  • 1. Delta Lake basics and usage
  • 2. Connecting to external data sources
  • 3. Batch and streaming ingestion
Topic 5: Data Governance & Quality11%- Data quality and reliability
  • 1. Schema enforcement and evolution
  • 2. Data validation and quality checks
- Unity Catalog implementation
  • 1. Permissions and access control
  • 2. Data governance model

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최신 Databricks Certification Databricks-Certified-Data-Engineer-Associate 무료샘플문제 (Q111-Q116):

질문 # 111
A data engineer is running a Spark job that repeatedly accesses the same DataFrame in multiple transformations. To improve performance by storing intermediate results in memory across operations, which Spark technique should the engineer use?

정답:A


질문 # 112
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?

정답:B

설명:
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.


질문 # 113
Which of the following must be specified when creating a new Delta Live Tables pipeline?

정답:D

설명:
Option E is the correct answer because it is the only mandatory requirement when creating a new Delta Live Tables pipeline. A pipeline is a data processing workflow that contains materialized views and streaming tables declared in Python or SQL source files. Delta Live Tables infers the dependencies between these tables and ensures updates occur in the correct order. To create a pipeline, you need to specify at least one notebook library to be executed, which contains the Delta Live Tables syntax. You can also specify multiple libraries of different languages within your pipeline. The other options are optional or not applicable for creating a pipeline. Option A is not required, but you can optionally provide a key-value pair configuration to customize the pipeline settings, such as the storage location, the target schema, the notifications, and the pipeline mode. Option B is not applicable, as the DBU/hour cost is determined by the cluster configuration, not the pipeline creation. Option C is not required, but you can optionally specify a storage location for the output data from the pipeline. If you leave it empty, the system uses a default location. Option D is not required, but you can optionally specify a location of a target database for the written data, either in the Hive metastore or the Unity Catalog.


질문 # 114
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

정답:A

설명:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html


질문 # 115
A data engineer needs to migrate the Unity Catalog external Delta table catalog.schema.sales while meeting the following requirements:
Databricks must manage file cleanup after the table is dropped.
The migration must minimize downtime while retaining the same table name, permissions, and history.
Access must be enforced through the registered Unity Catalog table name.
Which action should the engineer take?

정답:A


질문 # 116
......

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