Databricks-Certified-Data-Engineer-Associate Valid Exam Notes - Databricks-Certified-Data-Engineer-Associate Valid Exam Papers

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

SectionWeightObjectives
Topic 1: 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. Understanding query plans
  • 2. Optimization strategies
Topic 2: Development and Ingestion30%- Data ingestion patterns and methods
  • 1. Batch and streaming ingestion
  • 2. Delta Lake basics and usage
  • 3. Connecting to external data sources
- Notebook development fundamentals
  • 1. Data exploration and validation
  • 2. Using PySpark and Spark SQL
Topic 3: 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
Topic 4: Productionizing Data Pipelines18%- 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
- Monitoring and troubleshooting
  • 1. Logging and error handling
  • 2. Pipeline reliability and recovery
Topic 5: Data Governance & Quality11%- Data quality and reliability
  • 1. Data validation and quality checks
  • 2. Schema enforcement and evolution
- Unity Catalog implementation
  • 1. Permissions and access control
  • 2. Data governance model

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q116-Q121):

NEW QUESTION # 116
A data engineer configures a Databricks Lakeflow Job for daily customer data processing:
- Entry task: Single notebook loads raw data
- Parallel tasks:
- SQL query task for cleansing
- Notebook task for feature engineering
- Pipeline task for model updates
- Exit task: Dashboard refresh (must run after all parallel tasks
complete)
- Requirement: Implement this dependency pattern using a DAG-based task graph Which task configuration ensures all parallel tasks complete before the dashboard refresh task runs?

Answer: D

Explanation:
A fan-in dependency makes the dashboard refresh task depend on all three parallel tasks.
Databricks runs the parallel tasks after the entry task, and the dashboard refresh starts only after every upstream dependency completes.


NEW QUESTION # 117
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?

Answer: B

Explanation:
A table is a data entity that is stored in a physical location and can be accessed by other data engineers in other sessions. A table can be created from one or more tables using the CREATE TABLE or CREATE TABLE AS SELECT commands. A table can also be registered from an existing DataFrame using the spark.catalog.createTable method. A table can be queried using SQL or DataFrame APIs. A table can also be updated, deleted, or appended using the MERGE INTO command or the DeltaTable API. References:
* Create a table
* Create a table from a query result
* Register a table from a DataFrame
* [Query a table]
* [Update, delete, or merge into a table]


NEW QUESTION # 118
A data engineer is reading a very large dataset that contains skewed key values. Some partitions contain significantly more records than others, causing uneven workload distribution across executors. Which Spark issue does this scenario describe?

Answer: D


NEW QUESTION # 119
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

Answer: C

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.
read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources


NEW QUESTION # 120
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

Answer: A

Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions- from-query/td-p/36696


NEW QUESTION # 121
......

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