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Databricks Certified Data Analyst Associate Exam Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which of the following describes how Databricks SQL should be used in relation to other business intelligence (BI) tools like Tableau, Power BI, and looker?

A. As a complementary tool for quick in-platform Bl work
B. As a complete replacement with additional functionality
C. As a complementary tool for professional-grade presentations
D. As an exact substitute with the same level of functionality
E. As a substitute with less functionality

Answer: A

Explanation:
Databricks SQL is not meant to replace or substitute other BI tools, but rather to complement them by providing a fast and easy way to query, explore, and visualize data on the lakehouse using the built-in SQL editor, visualizations, and dashboards. Databricks SQL also integrates seamlessly with popular BI tools like Tableau, Power BI, and Looker, allowing analysts to use their preferred tools to access data through Databricks clusters and SQL warehouses. Databricks SQL offers low-code and no-code experiences, as well as optimized connectors and serverless compute, to enhance the productivity and performance of BI workloads on the lakehouse. Reference: Databricks SQL, Connecting Applications and BI Tools to Databricks SQL, Databricks integrations overview, Databricks SQL: Delivering a Production SQL Development Experience on the Lakehouse

NEW QUESTION # 61
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

A. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
B. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
C. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
D. All files with the .customers extension are deleted.
E. Running SELECT * FROM accounts.customers will return all rows in the table.

Answer: B

Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

NEW QUESTION # 62
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. Histogram
B. IBar Chart
C. Line Chart
D. There is no default. The user must choose a visualization type.
E. Violin Chart

Answer: B

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 # 63
Delta Lake stores table data as a series of data files, but it also stores a lot of other information.
Which of the following is stored alongside data files when using Delta Lake?

A. None of these
B. Table metadata
C. Owner account information
D. Data summary visualizations
E. Table metadata, data summary visualizations, and owner account information

Answer: B

Explanation:
Delta Lake is a storage layer that enhances data lakes with features like ACID transactions, schema enforcement, and time travel. While it stores table data as Parquet files, Delta Lake also keeps a transaction log (stored in the _delta_log directory) that contains detailed table metadata.
This metadata includes:
Table schema
Partitioning information
Data file paths
Transactional operations like inserts, updates, and deletes
Commit history and version control
This metadata is critical for supporting Delta Lake's advanced capabilities such as time travel and efficient query execution. Delta Lake does not store data summary visualizations or owner account information directly alongside the data files.

NEW QUESTION # 64
Which statement about subqueries is correct?

A. Subqueries can be used like other built-in functions to transform data into different data types.
B. Subqueries can be used like other user-defined functions to transform data into different data types.
C. Subqueries are not available in Databricks SQL
D. Subqueries can retrieve data without requiring the creation of a table or view.

Answer: D

Explanation:
In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.

NEW QUESTION # 65
......

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