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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Pipeline Architecture | 15-20% | - Design data pipelines for batch and streaming - Understand ELT vs ETL patterns - Monitor and optimize pipeline performance - Implement incremental data processing |
| Topic 2: Apache Spark Data Processing Fundamentals | 20-25% | - Use Spark SQL for data processing - Create and use Spark DataFrames - Apply transformations and actions on DataFrames - Work with structured data types (arrays, maps, structs) |
| Topic 3: Delta Lake Fundamentals | 20-25% | - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables |
| Topic 4: Python for Data Engineering | 10-15% | - Use PySpark for data processing - Work with Spark APIs in Python - Implement user-defined functions (UDFs) |
| Topic 5: Lakehouse Platform Concepts | 10-15% | - Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits |
| Topic 6: Spark SQL and DataFrames | 15-20% | - Write and execute Spark SQL queries - Join and union DataFrames - Aggregate and group data - Handle null values and data quality |
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NEW QUESTION # 281
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?
Answer: A
Explanation:
To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.
Reference Source: Databricks Unity Catalog Privilege Model - "Privileges Required to Create a Table."
NEW QUESTION # 282
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: E
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 # 283
A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several json files from containers on a storage account as soon as the file arrives within the storage account.
Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?
Answer: B
NEW QUESTION # 284
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?
Answer: B
NEW QUESTION # 285
A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.
Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?
Answer: D
Explanation:
In Databricks, you can use different languages within the same notebook by using magic commands. Magic commands are special commands that start with a percentage sign (%) and allow you to change the behavior of the cell. To use SQL within a cell of a Python notebook, you can add %sql to the first line of the cell. This will tell Databricks to interpret the rest of the cell as SQL code and execute it against the default database.
You can also specify a different database by using the USE statement. The result of the SQL query will be displayed as a table or a chart, depending on the output mode. You can also assign the result to a Python variable by using the -o option. For example, %sql -o df SELECT * FROM my_table will run the SQL query and store the result as a pandas DataFrame in the Python variable df. Option A is incorrect, as it is possible to use SQL in a Python notebook using magic commands. Option B is incorrect, as attaching the cell to a SQL endpoint is not necessary and will not change the language of the cell. Option C is incorrect, as simply writing SQL syntax in the cell will result in a syntax error, as the cell will still be interpreted as Python code. Option E is incorrect, as changing the default language of the notebook to SQL will affect all the cells, not just one. References: Use SQL in Notebooks - Knowledge Base - Noteable, [SQL magic commands - Databricks],
[Databricks SQL Guide - Databricks]
NEW QUESTION # 286
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