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

Section	Objectives
Data Processing and Transformations	- PySpark DataFrame transformations - User-defined functions (UDFs) - Delta Lake fundamentals (tables, transactions, optimization) - Apache Spark SQL operations (joins, aggregations, filtering)
Data Governance and Quality	- Data quality concepts and management - Unity Catalog basics - Data access control and governance
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - Handling structured and semi-structured data - ETL patterns and transformations
Databricks Lakehouse Platform Fundamentals	- Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts
Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Scheduling and monitoring jobs - Pipeline deployment and operationalization

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
B. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
C. They can reduce the cluster size of the SQL endpoint.
D. They can turn on the Auto Stop feature for the SQL endpoint.
E. They can set up the dashboard's SQL endpoint to be serverless.

Answer: E

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. Reference:
Create a serverless SQL endpoint
Assign a SQL endpoint to a dashboard
SQL Analytics API

NEW QUESTION # 175
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?

A. They can increase the maximum bound of the SQL endpoint's scaling range.
B. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
C. They can turn on the Auto Stop feature for the SQL endpoint.
D. They can increase the cluster size of the SQL endpoint.
E. They can turn on the Serverless feature for the SQL endpoint.

Answer: C

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

NEW QUESTION # 176
A data engineer team has decided to implement a new data platform on Databricks and is currently deciding how to store each kind of data on each data layer. What is the appropriate layer and data pairing for medallion architecture?

A. Gold Layer - Deduplicated money transfer transaction
B. Silver Layer - Cleansed master customer data
C. Silver Layer - Raw data from deposit account application
D. Bronze Layer - Summary of cash deposit amount for each country and city

Answer: B

Explanation:
In the Medallion architecture, the Silver layer holds cleansed and conformed data (such as master customer data), which has been cleaned, validated, and standardized after ingestion from the Bronze layer.

NEW QUESTION # 177
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 left join from orders_df to customers_df.
B. Perform an inner join between orders_df and customers_df using the customer identifier.
C. Perform a left join from customers_df to orders_df.
D. Perform a cross join between orders_df and customers_df.

Answer: B

NEW QUESTION # 178

Calculate the total sales amount for each region and store the results in a new dataframe called region_sales.
Given the expected result:

Which code will generate the expected result?

A. region_sales = sales_df.groupBy("region").agg(sum("sales_amountM).alias("total_sales_amount"))
B. region sales - sales_df.agg(sum("sales_amount").groupBy("region").alias("total sales amount"))
C. region_sales = sales_df. sum ("salen_aiTiount") . groupBy ("region") .alias ("total_sale3_amount")
D. region_sales= sales_df.groupBy("category").sum(nsales_amount").alias("t_otal_sales_amounl")

Answer: A

NEW QUESTION # 179
......

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