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Databricks Certified Data Analyst Associate Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
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. Combining two data sources into a single, comprehensive dataset
  • B. Segmenting customers into like groups using a clustering algorithm
  • C. Testing the quality of data as it is imported from a source
  • D. Automating complex notebook-based workflows with multiple tasks
  • E. Tracking usage of feature variables for machine learning projects

Answer: A

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 # 28
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

  • A. A markdown-based tile must be added to the top of the dashboard displaying the dashboard's name.
  • B. All data visualizations and counters must be created using Queries.
  • C. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
  • D. The dashboard owner must also be the owner of the queries, data visualizations, and counters.

Answer: B

Explanation:
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
Develop Queries:
For each desired visualization or counter, write a SQL query that retrieves the necessary data.
Create Visualizations and Counters:
After executing each query, utilize the results to create corresponding visualizations or counters. Databricks SQL offers a variety of visualization types to represent data effectively.
Assemble the Dashboard:
Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.


NEW QUESTION # 29
A data analyst has been asked to produce a visualization that shows the flow of users through a website.
Which of the following is used for visualizing this type of flow?

  • A. Word Cloud
  • B. IChoropleth
  • C. Pivot Table
  • D. Heatmap
  • E. Sankey

Answer: E

Explanation:
A Sankey diagram is a type of visualization that shows the flow of data between different nodes or categories. It is often used to represent the movement of users through a website, as it can show the paths they take, the sources they come from, the pages they visit, and the outcomes they achieve. A Sankey diagram consists of links and nodes, where the links represent the volume or weight of the flow, and the nodes represent the stages or steps of the flow. The width of the links is proportional to the amount of flow, and the color of the links can indicate different attributes or segments of the flow. A Sankey diagram can help identify the most common or popular user journeys, the bottlenecks or drop-offs in the flow, and the opportunities for improvement or optimization. Reference: The answer can be verified from Databricks documentation which provides examples and instructions on how to create Sankey diagrams using Databricks SQL Analytics and Databricks Visualizations. Reference links: Databricks SQL Analytics - Sankey Diagram, Databricks Visualizations - Sankey Diagram


NEW QUESTION # 30
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. The data analyst needs to upload this dataset for use as a table in Databricks SQL.
Which approach should the data analyst use to quickly upload the file into a table for use in Databricks SOL?

  • A. Create a table by manually copying and pasting the data values into cloud storage and then importing the data to Databricks.
  • B. Create a table by uploading the file using the Create page within Databricks SQL
  • C. Create a table by uploading the file to cloud storage and then importing the data to Databricks.
  • D. Create a table via a connection between Databricks and the desktop facilitated by Partner Connect.

Answer: B

Explanation:
Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:
Access the Data Upload Interface:
In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
Select Create or modify a table.
Upload the CSV File:
Click on the browse button or drag and drop the CSV file directly onto the designated area.
The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.
Configure Table Settings:
After uploading, a preview of the data is displayed.
Specify the table name, select the appropriate schema, and configure any additional settings as needed.
Create the Table:
Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.


NEW QUESTION # 31
Where in the Databricks SQL workspace can a data analyst configure a refresh schedule for a query when the query is not attached to a dashboard or alert?

  • A. The Dashboard Editor
  • B. The Query Editor
  • C. Data bxplorer
  • D. The Visualization editor

Answer: B

Explanation:
In Databricks SQL, to configure a refresh schedule for a query that is not attached to a dashboard or alert, a data analyst should use the Query Editor. Within the Query Editor, there is an option to set up scheduled executions for queries. This feature enables the query to run at specified intervals, ensuring that the results are updated regularly. By scheduling queries in this manner, analysts can automate data refreshes and maintain up-to-date query results without manual intervention.


NEW QUESTION # 32
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