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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Architecture | 15-20% | - Monitor and optimize pipeline performance - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Implement incremental data processing |
| Topic 2: Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Understand the Lakehouse architecture and its benefits - Describe key Databricks Lakehouse platform components |
| Topic 3: Apache Spark Data Processing Fundamentals | 20-25% | - Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Create and use Spark DataFrames |
| Topic 4: Python for Data Engineering | 10-15% | - Implement user-defined functions (UDFs) - Use PySpark for data processing - Work with Spark APIs in Python |
| Topic 5: Delta Lake Fundamentals | 20-25% | - Explain Delta Lake features and benefits - Create and manage Delta tables - Understand ACID transactions and time travel - Write to and read from Delta tables |
| Topic 6: Spark SQL and DataFrames | 15-20% | - Handle null values and data quality - Join and union DataFrames - Write and execute Spark SQL queries - Aggregate and group data |
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NEW QUESTION # 60
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?
Answer: A
Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches 100. To set up a webhook alert destination, the data engineer needs to do the following steps:
In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
Click Alert Destinations and click Add New Alert Destination.
Select Webhook and enter the webhook URL and the optional custom template for the notification message.
Click Create to save the webhook alert destination.
In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. Reference: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.
NEW QUESTION # 61
A Python file is ready for production and the client wants to use the most efficient yet cost- effective type of cluster possible. The workload is quite small, only processing 10GBs of data with only simple joins and no complex aggregations or wide transformations.
Which cluster meets the requirement?
Answer: C
Explanation:
A job cluster is the most cost-effective option for production workloads because it is created only for the duration of the job and then terminated. Enabling spot instances further reduces cost, and the small, simple workload does not require advanced performance optimizations.
NEW QUESTION # 62
Which of the following SQL keywords can be used to convert a table from a long format to a wide format?
Answer: C
Explanation:
The SQL keyword that can be used to convert a table from a long format to a wide format is PIVOT. The PIVOT clause is used to rotate the rows of a table into columns of a new table1. The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1. The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2. For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.
The other options are not suitable for converting a table from a long format to a wide format. CONVERT is a function that can be used to change the data type of an expression3. WHERE is a clause that can be used to filter the rows of a table based on a condition4. TRANSFORM is a keyword that can be used to apply a user- defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.
1: PIVOT | Databricks on AWS
2: Reshaping Data - Long vs Wide Format | Databricks on AWS
3: CONVERT | Databricks on AWS
4: WHERE | Databricks on AWS
5: TRANSFORM | Databricks on AWS
[SUM | Databricks on AWS]
NEW QUESTION # 63
A data engineer has multiple Unity Catalog-managed Delta tables that require regular maintenance. Currently, the data engineer manually schedules OPTIMIZE and VACUUM jobs for each table, adjusting the frequency based on how often each table is queried and updated. The engineer needs to eliminate this manual maintenance overhead and have the platform automatically determine when and how to run these operations. Which action should the engineer take to meet the requirements?
Answer: A
Explanation:
Predictive optimization automatically determines when Unity Catalog-managed tables need maintenance and runs operations such as OPTIMIZE and VACUUM. It can be enabled at the account, catalog, schema, or table level.
NEW QUESTION # 64
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?
Answer: E
Explanation:
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the
%sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]
NEW QUESTION # 65
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