Databricks-Certified-Data-Engineer-Associate Latest Mock Exam - Databricks Databricks Certified Data Engineer Associate Exam - Latest Exam Databricks-Certified-Data-Engineer-Associate Blueprint

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

Section	Weight	Objectives
Data Governance & Quality	11%	- 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
Development and Ingestion	30%	- Data ingestion patterns and methods 1. Delta Lake basics and usage 2. Batch and streaming ingestion 3. Connecting to external data sources - Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL
Databricks Intelligence Platform	10%	- 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 Processing & Transformations	31%	- Data transformation techniques 1. Complex data processing logic 2. DataFrame operations and transformations 3. Aggregations, joins, and window functions - Query optimization and performance 1. Understanding query plans 2. Optimization strategies
Productionizing Data Pipelines	18%	- Monitoring and troubleshooting 1. Logging and error handling 2. Pipeline reliability and recovery - Deployment and CI/CD 1. Databricks Asset Bundles 2. Version control integration - Workflow orchestration 1. Lakeflow Jobs creation and management 2. Scheduling, triggers, and dependencies

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q68-Q73):

NEW QUESTION # 68
A data engineer has configured a Lakeflow Job that runs daily to ingest customer transaction data from a legacy relational database. The extracted data must be written directly to a Unity Catalog table and be immediately queryable via SQL. The team needs to ensure full data lineage is preserved. Which action enables this data ingestion with direct landing in Unity Catalog, preserved lineage, and immediate SQL query ability?

A. Extract to Pandas DataFrame, convert to Spark, then save() to external path
B. Use ODBC pooling to extract all records in one batch and write to /dbfs/
C. Use spark.read.format("jdbc") and write with saveAsTable("catalog.schema.table")
D. Stage data in cloud storage via REST API, then load with a separate job

Answer: C

Explanation:
spark.read.format("jdbc") loads the relational data into a Spark DataFrame, and saveAsTable("catalog.schema.table") writes it directly as a Unity Catalog table that is immediately queryable with SQL. Unity Catalog captures lineage for the Databricks read/write operation.

NEW QUESTION # 69
A data engineer needs to parse only png files in a directory that contains files with different suffixes. Which code should the data engineer use to achieve this task?

Answer: B

NEW QUESTION # 70
A data engineer has two DataFrames:
orders_df, containing all customer orders
customers_df, containing only active customers
The engineer needs to remove all orders placed by inactive customers.
Which operation achieves this requirement?

A. Perform a cross join between orders_df and customers_df.
B. Perform a left join from customers_df to orders_df.
C. Perform a left join from orders_df to customers_df.
D. Perform an inner join between orders_df and customers_df using the customer identifier.

Answer: D

NEW QUESTION # 71
A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

A. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;
B. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;
C. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;
D. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;
E. CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;

Answer: C

NEW QUESTION # 72
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

A. The HISTORY command was run on the table
B. The VACUUM command was run on the table
C. The OPTIMIZE command was nun on the table
D. The TIME TRAVEL command was run on the table
E. The DELETE HISTORY command was run on the table

Answer: E

NEW QUESTION # 73
......

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