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

SectionWeightObjectives
Topic 1: Development and Ingestion30%- Notebook development fundamentals
  • 1. Using PySpark and Spark SQL
  • 2. Data exploration and validation
- Data ingestion patterns and methods
  • 1. Delta Lake basics and usage
  • 2. Connecting to external data sources
  • 3. Batch and streaming ingestion
Topic 2: Productionizing Data Pipelines18%- Monitoring and troubleshooting
  • 1. Pipeline reliability and recovery
  • 2. Logging and error handling
- 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
Topic 3: 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
Topic 4: Data Processing & Transformations31%- Data transformation techniques
  • 1. Aggregations, joins, and window functions
  • 2. Complex data processing logic
  • 3. DataFrame operations and transformations
- Query optimization and performance
  • 1. Optimization strategies
  • 2. Understanding query plans
Topic 5: Databricks Intelligence Platform10%- 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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q305-Q310):

NEW QUESTION # 305
A streaming pipeline reads IoT sensor data and writes aggregated results to a Delta table. The engineer wants to ensure that late-arriving records within a certain time window are still processed correctly. Which Structured Streaming feature should be used?

Answer: A


NEW QUESTION # 306
Which TWO items are characteristics of the Gold Layer?
Choose 2 answers

Answer: C,E


NEW QUESTION # 307
A data engineer needs to audit who made changes to a Delta table during the last seven days.
Which SQL statement returns the record of past operations, including timestamps, users, and operation types?

Answer: C

Explanation:
DESCRIBE HISTORY my_table returns the Delta table's operation history. Its result includes information such as the table version, operation timestamp, user identity when available, operation name, parameters, notebook or job details, and associated metrics. The engineer can filter the returned timestamps to analyze operations performed during the last seven days. SHOW TBLPROPERTIES displays configured table properties rather than transactional history. DESCRIBE DETAIL returns the current table metadata and storage characteristics, not a chronological audit of changes. Directly querying the _delta_log path is unnecessary, depends on file-level implementation details, and the displayed path is not the supported SQL statement for this audit requirement. Therefore, option C provides the supported and structured record of past Delta table operations.


NEW QUESTION # 308
Which query is performing a streaming hop from raw data to a Bronze table?

Answer: B

Explanation:
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:
* Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
* Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
* Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
* Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.
References:Databricks documentation on Structured Streaming: Structured Streaming in Databricks


NEW QUESTION # 309
A data engineer wants to create an external table in Databricks that references data stored in an Azure Data Lake Storage (ADLS) location. The goal is to enable Databricks to access and query this external data without moving it into the Databricks-managed storage. Which step should the data engineer take to successfully create the external table?

Answer: A

Explanation:
To reference data stored outside of Databricks-managed storage, the engineer should use CREATE TABLE ... LOCATION 'path', which creates an unmanaged (external) table pointing to the ADLS data without moving it into Databricks storage.


NEW QUESTION # 310
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