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The Databricks Databricks-Certified-Data-Engineer-Associate exam has 50 multiple-choice questions, and the candidate has 90 minutes to complete it. It is an online exam that can be taken at any time and from anywhere in the world. The questions in the exam are designed to test the candidate's ability to handle real-world data engineering challenges. Databricks-Certified-Data-Engineer-Associate exam also tests the candidate's understanding of the Databricks platform and how it can be used to solve data engineering problems.
The Databricks Databricks-Certified-Data-Engineer-Associate exam consists of 60 multiple-choice questions and is designed to test your understanding of Databricks concepts, including data engineering, data processing, data storage, and data analysis. To pass the exam, you must score at least 70%. Databricks-Certified-Data-Engineer-Associate Exam can be taken online and is available in several languages, including English, Spanish, and Chinese. Upon passing the exam, you will receive a Databricks Certified Data Engineer Associate certification, which is recognized globally as a mark of expertise in big data processing using Databricks.
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Databricks-Certified-Data-Engineer-Associate exam is an essential certification for data engineers who work with Databricks. Databricks-Certified-Data-Engineer-Associate Exam measures the candidate's knowledge, skills, and abilities in Databricks architecture, data ingestion, data processing, data engineering, data storage, and data management. Databricks Certified Data Engineer Associate Exam certification is recognized globally and is a valuable asset for data engineers who want to advance their careers and demonstrate their proficiency in Databricks.
NEW QUESTION # 110
A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".
Today, the data engineer runs the following command to complete this task:
After running the command today, the data engineer notices that the number of records in table transactions has not changed.
Which of the following describes why the statement might not have copied any new records into the table?
Answer: C
Explanation:
The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest. To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2. References: 1: COPY INTO | Databricks on AWS 2: Get started using COPY INTO to load data | Databricks on AWS
NEW QUESTION # 111
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?
Answer: E
Explanation:
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.
References:
* Managing Tables
* [Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 112
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?
Answer: A
NEW QUESTION # 113
A data engineer runs VACUUM orders RETAIN 72 HOURS on a Delta table and the command fails with an error about the retention interval.
What is the most likely cause?
Answer: B
Explanation:
Databricks blocks VACUUM with a retention period shorter than the default 168 hours (7 days) because it can break concurrent readers and time travel. The check is controlled by spark.databricks.delta.retentionDurationCheck.enabled, which should only be disabled with a clear understanding of the risk.
NEW QUESTION # 114
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 command can be used to grant full permissions on the database to the new data engineering team?
Answer: C
Explanation:
To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:
GRANT ALL PRIVILEGES ON TABLE sales TO team;
This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.
References:Databricks documentation on SQL permissions: SQL Permissions in Databricks
NEW QUESTION # 115
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