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| Section | Objectives |
|---|---|
| Topic 1: Data Management in Lakehouse | - Delta Lake fundamentals
|
| Topic 2: Databricks SQL and SQL Analytics | - Data transformation and analysis
|
| Topic 3: Data Visualization and Dashboards | - Creating dashboards in Databricks SQL
|
| Topic 4: Data Governance and Security | - Access control and permissions
|
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NEW QUESTION # 109
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?
Answer: B
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 # 110
An analyst has been asked to combine the data in two tables: suppliers and new_suppliers. It is possible that some of the supplier_id values match in both tables, meaning those suppliers have already been added to the suppliers table. If that is the case, the data should be unchanged.
Which command will combine the two tables without duplicating the rows with the same supplier_id?
Answer: B
Explanation:
The correct answer is A because MERGE INTO is the Databricks SQL command used to combine source rows with a target table based on a match condition. The condition checks whether supplier_id already exists.
If a row does not match, WHEN NOT MATCHED THEN INSERT * inserts it. If a row already matches, no WHEN MATCHED action is provided, so the existing row remains unchanged. This prevents duplicate supplier rows.
Official documentation extract used: Databricks shows the pattern MERGE INTO target USING source ON target.key = source.key WHEN NOT MATCHED THEN INSERT *.
NEW QUESTION # 111
A data analyst has two data sources that are providing similar but complementary information. The analyst wants to combine these sources of data into a single, comprehensive dataset for ongoing use for their team in a variety of different projects.
Which term is used to describe this type of work?
Answer: D
Explanation:
Option D is correct. The scenario describes combining multiple complementary data sources into one comprehensive dataset. That is data blending. Last-mile ETL is usually project-specific final transformation near the end of an analytics workflow, while this question emphasizes combining two source datasets for broader ongoing team use. The current official Databricks exam guide describes the same kind of capability as creating unified datasets by joining data from multiple sources. That aligns with the concept of data blending. Reference: Databricks Certified Data Analyst Associate Exam Guide.
NEW QUESTION # 112
A data analyst has been asked to count the number of customers in each region and has written the following query:
If there is a mistake in the query, which of the following describes the mistake?
Answer: B
Explanation:
In the provided SQL query, the data analyst is trying to count the number of customers in each region.
However, they made a mistake by not including the "GROUP BY" clause to group the results by region.
Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. References: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.
I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a "customers" table and order the results by region. There's no visible syntax highlighting or any other color - it's monochromatic. The query is the same as the one in your question. I'm not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:
SELECT region, count(*) AS number_of_customers
FROM customers
ORDER BY region;
This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.
#
NEW QUESTION # 113
A data analyst has written and saved a series of queries that reveal trends that need to be monitored by several stakeholders.
Which tool should the data analyst use to share the results of all of the queries to be viewed at once?
Answer: A
Explanation:
The correct answer is C because a dashboard is designed to collect multiple visualizations and query outputs in one shared reporting interface. A SQL warehouse provides compute for running SQL queries, but it is not a sharing/reporting interface. Query History only shows past query executions. A visualization tab belongs to one query result, while a dashboard can display multiple results together for stakeholders.
Official documentation extract used: Databricks states that dashboards are used to "build data visualizations and share reports with your team."
NEW QUESTION # 114
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