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

SectionWeightObjectives
Data Processing & Transformations31%- Data transformation techniques
  • 1. DataFrame operations and transformations
  • 2. Complex data processing logic
  • 3. Aggregations, joins, and window functions
- Query optimization and performance
  • 1. Understanding query plans
  • 2. Optimization strategies
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
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
Data Governance & Quality11%- Data quality and reliability
  • 1. Schema enforcement and evolution
  • 2. Data validation and quality checks
- Unity Catalog implementation
  • 1. Data governance model
  • 2. Permissions and access control
Development and Ingestion30%- Data ingestion patterns and methods
  • 1. Delta Lake basics and usage
  • 2. Connecting to external data sources
  • 3. Batch and streaming ingestion
- Notebook development fundamentals
  • 1. Data exploration and validation
  • 2. Using PySpark and Spark SQL

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q243-Q248):

質問 # 243
A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

正解：A

解説：
Explanation
https://docs.databricks.com/delta-live-tables/expectations.html
Delta Live Tables is a tool provided by Databricks that can help data engineers automate the monitoring of data quality. It is designed for managing data pipelines, monitoring data quality, and automating workflows.
With Delta Live Tables, you can set up data quality checks and alerts to detect issues and anomalies in your data as it is ingested and processed in real-time. It provides a way to ensure that the data quality meets your desired standards and can trigger actions or notifications when issues are detected. While the other tools mentioned may have their own purposes in a data engineeringenvironment, Delta Live Tables is specifically designed for data quality monitoring and automation within the Databricks ecosystem.


質問 # 244
Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

正解：C

解説：
In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. References: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?


質問 # 245
A data architect has determined that a table of the following format is necessary:

Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?

正解：E

解説：
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks


質問 # 246
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

正解：E

解説：
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. By using a data lakehouse, both the data analysis and data engineering teams can access the same data sources and formats, ensuring data consistency and quality across their reports. A data lakehouse also supports schema enforcement and evolution, data validation, and time travel to old table versions, which can help resolve data conflicts and errors1. References: 1: What is a Data Lakehouse? - Databricks 2: What is a data lakehouse? | IBM


質問 # 247
Which SQL code snippet will correctly demonstrate a Data Definition Language (DDL) operation used to create a table?

正解：A


質問 # 248
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