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| Section | Weight | Objectives |
|---|
| Data Processing & Transformations | 31% | - Query optimization and performance
- 1. Understanding query plans
- 2. Optimization strategies
- Data transformation techniques
- 1. Aggregations, joins, and window functions
- 2. DataFrame operations and transformations
- 3. Complex data processing logic
|
| Productionizing Data Pipelines | 18% | - Monitoring and troubleshooting
- 1. Logging and error handling
- 2. Pipeline reliability and recovery
- Workflow orchestration
- 1. Lakeflow Jobs creation and management
- 2. Scheduling, triggers, and dependencies
- Deployment and CI/CD
- 1. Version control integration
- 2. Databricks Asset Bundles
|
| Development and Ingestion | 30% | - Data ingestion patterns and methods
- 1. Connecting to external data sources
- 2. Delta Lake basics and usage
- 3. Batch and streaming ingestion
- Notebook development fundamentals
- 1. Data exploration and validation
- 2. Using PySpark and Spark SQL
|
| Databricks Intelligence Platform | 10% | - Platform architecture and core concepts
- 1. Data layout and optimization: partitioning, file sizing, caching
- 2. Compute options: clusters, SQL warehouses, serverless
- 3. Workspace navigation and management
|
| Data Governance & Quality | 11% | - Unity Catalog implementation
- 1. Permissions and access control
- 2. Data governance model
- Data quality and reliability
- 1. Schema enforcement and evolution
- 2. Data validation and quality checks
|
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Databricks Certified Data Engineer Associate Exam Sample Questions (Q249-Q254):
NEW QUESTION # 249
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?
- A. GRANT USAGE ON TABLE sales TO team;
- B. GRANT ALL PRIVILEGES ON TABLE sales TO team;
- C. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
- D. GRANT SELECT ON TABLE sales TO team;
- E. GRANT ALL PRIVILEGES ON TABLE team TO sales;
Answer: B
Explanation:
To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON TABLE statement. This statement will grant all the possible privileges on the table, such as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on the table team to the user or group sales, which is the opposite of what the question asks. References: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a table using SQL - Azure Databricks, SQL Privileges - Databricks
NEW QUESTION # 250
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. Raw layer
- B. Gold
- C. Silver
- D. Bronze
Answer: D
Explanation:
In the Medallion architecture, the Bronze layer stores raw ingested data, including historical event data from Kafka. This approach preserves the full history at lower cost while allowing further refinement into Silver and Gold layers.
NEW QUESTION # 251
A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines.
The data engineer wants to avoid the operational overhead of managing and tuning clusters.
Which architectural solution will meet the requirements?
- A. Deploy a dedicated, manually managed cluster optimized by in-house IT staff.
- B. Implement a hybrid approach with scheduled batch jobs on custom cloud VMs.
- C. Utilize Databricks serverless compute that automatically optimizes resources and abstracts cluster management.
- D. Use an auto-scaling cluster configured and monitored by the user.
Answer: C
Explanation:
Databricks serverless compute automatically handles cluster provisioning, scaling, and optimization, reducing operational overhead while providing high availability and reliability. This makes it the best choice for meeting stringent SLAs that require minimal runtime errors and consistent performance.
NEW QUESTION # 252
Which of the following describes the relationship between Bronze tables and raw data?
- A. Bronze tables contain raw data with a schema applied.
- B. Bronze tables contain more truthful data than raw data.
- C. Bronze tables contain aggregates while raw data is unaggregated.
- D. Bronze tables contain a less refined view of data than raw data.
- E. Bronze tables contain less data than raw data files.
Answer: A
Explanation:
Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures
"as-is", along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. References: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.
NEW QUESTION # 253
A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines. The data engineer wants to avoid the operational overhead of managing and tuning clusters.
Which architectural solution will meet the requirements?
- A. Deploy a dedicated, manually managed cluster optimized by in-house IT staff.
- B. Implement a hybrid approach with scheduled batch jobs on custom cloud VMs.
- C. Utilize Databricks serverless compute that automatically optimizes resources and abstracts cluster management.
- D. Use an auto-scaling cluster configured and monitored by the user.
Answer: C
NEW QUESTION # 254
......
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