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Databricks Certified Data Analyst Associate Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Business analyst
B. Business intelligence analyst
C. SQL analyst
D. Data analyst
E. Data engineer

Answer: E

Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

NEW QUESTION # 15
A data analyst wants to create a dashboard with three main sections: Development, Testing, and Production. They want all three sections on the same dashboard, but they want to clearly designate the sections using text on the dashboard.
Which of the following tools can the data analyst use to designate the Development, Testing, and Production sections using text?

A. Markdown-based text boxes
B. Separate endpoints for each section
C. Direct text written into the dashboard in editing mode
D. Separate queries for each section
E. Separate color palettes for each section

Answer: A

Explanation:
Markdown-based text boxes are useful as labels on a dashboard. They allow the data analyst to add text to a dashboard using the %md magic command in a notebook cell and then select the dashboard icon in the cell actions menu. The text can be formatted using markdown syntax and can include headings, lists, links, images, and more. The text boxes can be resized and moved around on the dashboard using the float layout option. Reference: Dashboards in notebooks, How to add text to a dashboard in Databricks

NEW QUESTION # 16
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?

A. A quantitative variable that can take on an uncountable set of values
B. A quantitative variable Chat can take on a finite or countably infinite set of values
C. A categorical variable in which the number of categories continues to increase over time
D. A quantitative variable that never stops changing

Answer: A

Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.

NEW QUESTION # 17
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. All files with the .customers extension are deleted.
C. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
D. Running SELECT * FROM accounts.customers will return all rows in the table.
E. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.

Answer: C

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 # 18
A data analyst has created a Query in Databricks SQL, and now wants to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which step will the data analyst need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. Alter the Query to return two separate sets of results.
B. Add two separate visualizations to the dashboard based on the same Query.
C. Decide on a single data visualization to add to the dashboard.
D. Copy the Query and create one data visualization per query.

Answer: B

NEW QUESTION # 19
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