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| Section | Weight | Objectives |
|---|---|---|
| Lakehouse Platform Concepts | 10-15% | - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components |
| Data Pipeline Architecture | 15-20% | - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Implement incremental data processing - Monitor and optimize pipeline performance |
| Python for Data Engineering | 10-15% | - Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs) |
| Delta Lake Fundamentals | 20-25% | - Write to and read from Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables |
| Apache Spark Data Processing Fundamentals | 20-25% | - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Create and use Spark DataFrames - Apply transformations and actions on DataFrames |
| Spark SQL and DataFrames | 15-20% | - Aggregate and group data - Handle null values and data quality - Write and execute Spark SQL queries - Join and union DataFrames |
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NEW QUESTION # 173
In a Lakeflow Declarative Pipeline (Delta Live Tables), a data engineer must ensure that records with a null order_id are discarded from the silver table while the pipeline continues to run.
Which expectation clause should be used?
Answer: B
NEW QUESTION # 174
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
Answer: B
Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
References: Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL
NEW QUESTION # 175
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?
Answer: C
Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html
NEW QUESTION # 176
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
Answer: D
Explanation:
Explanation
CREATE TABLE new_employees_table
USING JDBC
OPTIONS (
url "<jdbc_url>",
dbtable "<table_name>",
user '<username>',
password '<password>'
) AS
SELECT * FROM employees_table_vw
https://docs.databricks.com/external-data/jdbc.html#language-sql
NEW QUESTION # 177
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?
Answer: A
Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL
NEW QUESTION # 178
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