Databricks-Certified-Data-Engineer-Associate Study Center | Actual Databricks-Certified-Data-Engineer-Associate Test Answers

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

Section	Weight	Objectives
Data Processing & Transformations	31%	- Query optimization and performance 1. Optimization strategies 2. Understanding query plans - Data transformation techniques 1. Complex data processing logic 2. DataFrame operations and transformations 3. Aggregations, joins, and window functions
Databricks Intelligence Platform	10%	- Platform architecture and core concepts 1. Data layout and optimization: partitioning, file sizing, caching 2. Workspace navigation and management 3. Compute options: clusters, SQL warehouses, serverless
Data Governance & Quality	11%	- Unity Catalog implementation 1. Permissions and access control 2. Data governance model - Data quality and reliability 1. Data validation and quality checks 2. Schema enforcement and evolution
Productionizing Data Pipelines	18%	- 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. Databricks Asset Bundles 2. Version control integration
Development and Ingestion	30%	- Notebook development fundamentals 1. Data exploration and validation 2. Using PySpark and Spark SQL - Data ingestion patterns and methods 1. Batch and streaming ingestion 2. Connecting to external data sources 3. Delta Lake basics and usage

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?

A. The previous day's file has already been copied into the table.
B. The format of the files to be copied were not included with the FORMAT_OPTIONS keyword.
C. The COPY INTO statement requires the table to be refreshed to view the copied rows.
D. The PARQUET file format does not support COPY INTO.
E. The names of the files to be copied were not included with the FILES keyword.

Answer: A

NEW QUESTION # 58
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?

A. Option A
B. Option D
C. Option C
D. Option E
E. Option B

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 # 59
A data engineer is joining a very large events_df (billions of rows) with a small country_lookup_df (fewer than 10,000 rows) on the country_code column. The join is slow due to data shuffling. How should the data engineer improve performance?

A. Persist both DataFrames, then run the same join
B. broadcast events_df when performing the join
C. Cache both DataFrames, then run the same join
D. broadcast country_lookup_df when performing the join

Answer: D

Explanation:
Broadcasting country_lookup_df sends the small lookup table to each executor, avoiding a large shuffle of the billions-row events_df and improving join performance.

NEW QUESTION # 60
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

A. View
B. Spark SQL Table
C. Database
D. Temporary view
E. Delta Table

Answer: D

Explanation:
A temporary view is a relational object that is defined in the metastore and points to an existing DataFrame. It does not copy or store any physical data, but only saves the query that defines the view. The lifetime of a temporary view is tied to the SparkSession that was used to create it, so it does not persist across different sessions or applications. A temporary view is useful for accessing the same data multiple times within the same notebook or session, without incurring additional storage costs. The other options are either materialized (A, E), persistent (B, C), or not relational objects ©. References: Databricks Documentation - Temporary View, Databricks Community - How do temp views actually work?, Databricks Community - What's the difference between a Global view and a Temp view?, Big Data Programmers - Temporary View in Databricks.

NEW QUESTION # 61
A data engineer has developed a Python notebook in a Databricks workspace and has configured it to run as a scheduled Job to process daily sales data. How are the storage and execution of this notebook managed within the Databricks architecture?

A. The notebook is stored securely and encrypted in the control plane, and the code executes in the compute plane when the job runs.
B. The notebook is stored unencrypted in the workspace storage bucket, and the code executes on the cluster driver node in the compute plane when the job runs.
C. The notebook is stored securely and encrypted in the compute plane, and the code executes in the control plane when the job runs.
D. The notebook is stored securely and encrypted in Unity Catalog, and the code executes in Delta Lake when the job runs.

Answer: A

Explanation:
Databricks workspace notebooks and their metadata are securely stored and encrypted in the control plane. When the scheduled job runs, the notebook code is executed on Databricks compute in the compute plane, where data processing occurs.

NEW QUESTION # 62
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