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

SectionWeightObjectives
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
Data Processing & Transformations31%- Query optimization and performance
  • 1. Understanding query plans
  • 2. Optimization strategies
- Data transformation techniques
  • 1. Complex data processing logic
  • 2. Aggregations, joins, and window functions
  • 3. DataFrame operations and transformations
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
Productionizing Data Pipelines18%- Workflow orchestration
  • 1. Lakeflow Jobs creation and management
  • 2. Scheduling, triggers, and dependencies
- Monitoring and troubleshooting
  • 1. Pipeline reliability and recovery
  • 2. Logging and error handling
- Deployment and CI/CD
  • 1. Version control integration
  • 2. Databricks Asset Bundles
Development and Ingestion30%- Notebook development fundamentals
  • 1. Using PySpark and Spark SQL
  • 2. Data exploration and validation
- Data ingestion patterns and methods
  • 1. Connecting to external data sources
  • 2. Delta Lake basics and usage
  • 3. Batch and streaming ingestion

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
A data engineer has multiple Unity Catalog-managed Delta tables that require regular maintenance. Currently, the engineer manually schedules OPTIMIZE and VACUUM jobs for each table, adjusting their frequency according to how often each table is queried and updated.
The engineer needs to eliminate this manual maintenance overhead and allow Databricks to determine automatically when and how to run these operations.
Which action should the engineer take?

Answer: A


NEW QUESTION # 102
A Data Engineer is building a simple data pipeline using Delta Live Tables (DLT) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create a DLT pipeline that reads the raw JSON data and writes it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using DLT?

Answer: A

Explanation:
The correct approach is to use @dlt.table to define a Delta Live Table and read the raw JSON data with spark.read.json("s3://..."). This properly ingests the JSON source data and materializes it as a managed Delta table in DLT.


NEW QUESTION # 103
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?

Answer: D

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


NEW QUESTION # 104
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: B

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 # 105
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database. They run the following command:
CREATE TABLE jdbc_customer360
USING
OPTIONS (
url "jdbc:sqlite:/customers.db", dbtable "customer360"
)
Which line of code fills in the above blank to successfully complete the task?

Answer: B

Explanation:
To create a table in Databricks using data from an SQLite database, the correct syntax involves specifying the format of the data source. The format in the case of using JDBC (Java Database Connectivity) with SQLite is specified by the org.apache.spark.sql.jdbc format. This format allows Spark to interface with various relational databases through JDBC. Here is how the command should be structured:
CREATE TABLE jdbc_customer360
USING org.apache.spark.sql.jdbc
OPTIONS (
url "jdbc:sqlite:/customers.db",
dbtable "customer360"
)
The USING org.apache.spark.sql.jdbc line specifies that the JDBC data source is being used, enabling Spark to interact with the SQLite database via JDBC.
References:Databricks documentation on JDBC: Connecting to SQL Databases using JDBC


NEW QUESTION # 106
......

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