Databricks Updated Databricks-Certified-Data-Engineer-Associate Exam Questions and Answers by hasan

Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. References: Schedule a job, Jobs API, Databricks CLI, Cron expressions

Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1. References:

What is Delta Lake? | Databricks on AWS

Quickstart — Delta Lake Documentation

 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]