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Databricks-Certified-Data-Analyst-Associate Free Exam Dumps | Certification Databricks-Certified-Data-Analyst-Associate Questions

Databricks-Certified-Data-Analyst-Associate Free Exam Dumps | Certification Databricks-Certified-Data-Analyst-Associate Questions

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

  • A. Edit the Owner field in the table page by selecting All Users
  • B. Edit the Owner field in the table page by selecting the new owner's account
  • C. Edit the Owner field in the table page by removing their own account
  • D. Edit the Owner field in the table page by removing all access
  • E. Edit the Owner field in the table page by selecting the Admins group

Answer: B

Explanation:
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
A . Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
B . Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
D . Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
E . Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table


NEW QUESTION # 14
A data analyst is processing a complex aggregation on a table with zero null values and their query returns the following result:

Which of the following queries did the analyst run to obtain the above result?

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: E

Explanation:
The result set provided shows a combination of grouping by two columns (group_1 and group_2) with subtotals for each level of grouping and a grand total. This pattern is typical of a GROUP BY ... WITH ROLLUP operation in SQL, which provides subtotal rows and a grand total row in the result set.
Considering the query options:
A) Option A: GROUP BY group_1, group_2 INCLUDING NULL - This is not a standard SQL clause and would not result in subtotals and a grand total.
B) Option B: GROUP BY group_1, group_2 WITH ROLLUP - This would create subtotals for each unique group_1, each combination of group_1 and group_2, and a grand total, which matches the result set provided.
C) Option C: GROUP BY group_1, group 2 - This is a simple GROUP BY and would not include subtotals or a grand total.
D) Option D: GROUP BY group_1, group_2, (group_1, group_2) - This syntax is not standard and would likely result in an error or be interpreted as a simple GROUP BY, not providing the subtotals and grand total.
E) Option E: GROUP BY group_1, group_2 WITH CUBE - The WITH CUBE operation produces subtotals for all combinations of the selected columns and a grand total, which is more than what is shown in the result set.
The correct answer is Option B, which uses WITH ROLLUP to generate the subtotals for each level of grouping as well as a grand total. This matches the result set where we have subtotals for each group_1, each combination of group_1 and group_2, and the grand total where both group_1 and group_2 are NULL.


NEW QUESTION # 15
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the "Add visualization" button on the SQL Editor page, which of the following types of visualizations will be selected by default?

  • A. Violin Chart
  • B. Histogram
  • C. IBar Chart
  • D. There is no default. The user must choose a visualization type.
  • E. Line Chart

Answer: C

Explanation:
According to the Databricks SQL documentation, when a data analyst clicks on the "Add visualization" button on the SQL Editor page, the default visualization type is Bar Chart. This is because the result set has two columns: one of type STRING and one of type INT. The Bar Chart visualization automatically assigns the STRING column to the X-axis and the INT column to the Y-axis. The Bar Chart visualization is suitable for showing the distribution of a numeric variable across different categories. Reference: Visualization in Databricks SQL, Visualization types


NEW QUESTION # 16
A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.
Which of the following projects should be completed in Databricks SQL?

  • A. Automating complex notebook-based workflows with multiple tasks
  • B. Testing the quality of data as it is imported from a source
  • C. Combining two data sources into a single, comprehensive dataset
  • D. Tracking usage of feature variables for machine learning projects
  • E. Segmenting customers into like groups using a clustering algorithm

Answer: C

Explanation:
Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. Reference:
Databricks SQL overview
Databricks SQL use cases
Joining data sources


NEW QUESTION # 17
Which of the following statements about a refresh schedule is incorrect?

  • A. A query can be refreshed anywhere from 1 minute lo 2 weeks
  • B. A refresh schedule is not the same as an alert.
  • C. You must have workspace administrator privileges to configure a refresh schedule
  • D. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
  • E. Refresh schedules can be configured in the Query Editor.

Answer: C

Explanation:
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule-workspace administrator privileges are not required.
Here is the breakdown of the correct information:
A . True - Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
B . True - You can configure refresh schedules in the Query Editor.
C . False statement - A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
D . True - Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.
E . False (and thus the correct answer to this question) - You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.


NEW QUESTION # 18
......

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