The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table.
This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate,but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges - Delta Lake Documentation

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. Reference:
What is Delta Lake? | Databricks on AWS
Quickstart - Delta Lake Documentation

Explanation
The SQL keyword PIVOT can be used to convert a table from a long format to a wide format. A long format table has one column for each variable and one row for each observation. A wide format table has one column for each variable and value combination and one row for each observation. PIVOT allows you to specify the column that contains the values to be pivoted, the column that contains the categories to be pivoted, and the aggregation function to be applied to the values. For example, the following query converts a long format table of sales data into a wide format table with columns for each product and sum of sales:
SELECT *
FROM sales
PIVOT (
SUM(sales_amount) FOR product IN ('A', 'B', 'C')
)
References: The information can be referenced from Databricks documentation on SQL: PIVOT.
https://files.training.databricks.com/assessments/practice-exams/PracticeExam-DataEngineerAssociate.pdf
https://community.databricks.com/t5/data-engineering/practice-exams-for-databricks-certified-data-engineer/td-p

Explanation
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20a