Databricks-Certified-Data-Analyst-Associate Exam Dumps - Databricks Data Analyst Questions and Answers

Option B is correct. The problem is many users running small queries simultaneously, which is a concurrency issue. Increasing the maximum number of clusters lets the SQL warehouse scale out to serve more concurrent queries. Increasing cluster size is more useful for complex or resource-heavy queries, not necessarily many small simultaneous queries. Auto Stop reduces cost but does not improve active-query latency. Official Databricks extract: “You can increase the maximum clusters if you want to handle more concurrent users,” and Databricks recommends monitoring queued queries and adjusting maximum clusters.

 The SQL code provided in Option D is the correct way to create a view named sales_by_employee that will always stay up-to-date with the sales and employees tables. The code uses the CREATE OR REPLACE VIEW statement to define a new view that joins the sales and employees tables on the employee_id column. It selects the employee_name as sales_person and all sales for each employee, ensuring that the data entity/object is always up-to-date when new data are added to these tables.

The answer can be verified from Databricks SQL documentation which provides insights on creating views using SQL queries, joining tables, and selecting specific columns to be included in the view. Reference link: Databricks SQL

The approach that cannot be used to share the dashboard and meet the security requirement is D. Generating a Personal Access Token that is good for 1 day and sharing it with the client. This approach would give the client access to the Databricks workspace using the token owner’s identity and permissions, which could expose other information, resources, or artifacts in the database1. The other approaches can be used to share the dashboard and meet the security requirement because:

A. Downloading the Dashboard as a PDF and sharing it with the client would only provide a static snapshot of the dashboard without any interactive features or access to the underlying data2.

B. Setting a refresh schedule for the dashboard and entering the client’s email address in the “Subscribers” box would send the client an email with the latest dashboard results as an attachment or a link to a secure web page3. The client would not be able to access the Databricks workspace or the dashboard itself.

C. Taking a screenshot of the dashboard and sharing it with the client would also only provide a static snapshot of the dashboard without any interactive features or access to the underlying data4.

E. Downloading a PNG file of the visualizations in the dashboard and sharing them with the client would also only provide a static snapshot of the visualizations without any interactive features or access to the underlying data5. References:

1: Personal access tokens

2: Download as PDF

3: Automatically refresh a dashboard

4: Take a screenshot

5: Download a PNG file

Option C is correct. To retrieve changes from a remote central Git repository into a local Databricks Git folder/Repo, the correct Git operation is Pull. Push sends local commits to the remote repository. Commit records local changes. Clone creates a new local copy. Merge combines branches but is not the operation described here. Official Databricks extract: “To pull changes from the remote Git repository, click Pull.”

The reason the alert is not functioning as expected is because Databricks SQL Alerts do not support query parameters. This limitation applies to all types of parameters, including date parameters.

Here’s why:

Alerts require static, deterministic query results so they can compare values consistently during scheduled executions.

When a query includes parameters (e.g., a date range parameter), its results may change based on user input or the default value set in the query editor.

However, Databricks SQL Alerts will always use the default value set for the parameter at the time the alert is created. This means the alert doesn’t dynamically adapt to new date ranges and will not reflect changes unless the query is manually updated.

As a result, if the business logic behind the alert depends on changing date ranges or any user input, the alert will not trigger correctly, or may never trigger at all.

Therefore, the correct explanation contradicts Option B, which is incorrect in saying that alerts cannot work with date-based queries at all. In fact, they can—as long as the query is static (i.e., without parameters).