A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project. Which of the following commands can be used to grant full permissions on the database to the new data engineering team?
Correct Answer: A
To grant full permissions on a table to a user or a group, you can use the GRANT ALL PRIVILEGES ON TABLE statement. This statement will grant all the possible privileges on the table, such as SELECT, CREATE, MODIFY, DROP, ALTER, etc. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it does not grant all the possible privileges on the table, but only a subset of them. Option C is incorrect, as it only grants the SELECT privilege on the table, which is not enough to fully manage the project. Option D is incorrect, as it grants the USAGE privilege on the table, which is not a valid privilege for tables. Option E is incorrect, as it grants all the privileges on the table team to the user or group sales, which is the opposite of what the question asks. References: Grant privileges on a table using SQL | Databricks on AWS, Grant privileges on a table using SQL - Azure Databricks, SQL Privileges - Databricks
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results. Which of the following approaches can the manager use to ensure the results of the query are updated each day?
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command: DROP TABLE IF EXISTS my_table; After running this command, the engineer notices that the data files and metadata files have been deleted from the file system. Which of the following describes why all of these files were deleted?
Correct Answer: A
The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system. Option B is not correct, as the size of the table's data does not affect the behavior of dropping the table. Whether the table's data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external. Option C is not correct, for the same reason as option B. Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system. Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata. Reference: Managing Tables [Databricks Data Engineer Professional Exam Guide]
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos. Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
Correct Answer: D
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. Reference: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.
