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| Section | Objectives |
|---|---|
| Topic 1: Data Processing and Transformations | - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs) - Apache Spark SQL operations (joins, aggregations, filtering) |
| Topic 2: Productionizing Data Pipelines | - Scheduling and monitoring jobs - Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization |
| Topic 3: Data Governance and Quality | - Data quality concepts and management - Unity Catalog basics - Data access control and governance |
| Topic 4: Data Ingestion and ELT Development | - Handling structured and semi-structured data - Data ingestion using Spark SQL and PySpark - ETL patterns and transformations |
| Topic 5: Databricks Lakehouse Platform Fundamentals | - Clusters, notebooks, and basic Databricks environment usage - Workspace, architecture, and core platform concepts |
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NEW QUESTION # 81
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: B
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 # 82
A notebook task check_volume calculates a daily data volume and writes it to a Lakeflow Jobs taskValue named record_count. If the count is greater than 1 million, the job should run high_volume_pipeline; otherwise, it should run normal_pipeline. Which control-flow feature should be used?
Answer: C
Explanation:
An If/else condition task can evaluate {{tasks.check_volume.values.record_count}} > 1000000 and route execution to high_volume_pipeline for true or normal_pipeline for false. Run if evaluates task status, not numeric task values.
NEW QUESTION # 83
A data engineer has been given a new record of data:
id STRING = 'a1'
rank INTEGER = 6
rating FLOAT = 9.4
Which of the following SQL commands can be used to append the new record to an existing Delta table my_table?
Answer: E
Explanation:
To append a new record to an existing Delta table, you can use the INSERT INTO statement with the VALUES clause. This statement will insert one or more rows into the table with the specified values.
Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it uses the UNION operator, which will return a new table that is the union of two tables, not append to an existing table. Option C is incorrect, as it uses the INSERT VALUES statement, which is not a valid SQL syntax.
Option D is incorrect, as it uses the UPDATE statement, which will modify existing rows in the table, not append new rows. Option E is incorrect, as it uses the UPDATE VALUES statement, which is also not a valid SQL syntax. References: Insert data into a table using SQL | Databricks on AWS, Insert data into a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks
NEW QUESTION # 84
A data engineer uploads a CSV file using the "Create or modify a table using file upload" option in Databricks. To avoid incorrect schema inference, they disable Automatically detect column types before creating a Unity Catalog-managed table. What is the outcome?
Answer: B
Explanation:
Disabling Automatically detect column types causes Databricks to create every uploaded CSV column as STRING, avoiding automatic numeric, date, or Boolean inference.
NEW QUESTION # 85
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?
Answer: B
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. References:
* Create a serverless SQL endpoint
* Assign a SQL endpoint to a dashboard
* SQL Analytics API
NEW QUESTION # 86
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