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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

TopicDetails
Topic 1
  • SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 2
  • Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 3
  • Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints
  • warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint
  • warehouses, trade-off between cluster size and cost for Databricks SQL endpoints
  • warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
Topic 4
  • Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.
Topic 5
  • Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
What is an advantage of using a Delta Lake-based data lakehouse over classic enterprise data warehouse solutions?

Answer: D

Explanation:
Option A is correct. Classic enterprise data warehouses commonly use proprietary storage formats, while the Databricks lakehouse is built around open table formats such as Delta Lake and Iceberg. Databricks documentation states that Delta Lake is open source and that the Databricks platform uses no proprietary data formats to avoid vendor lock-in. Schema enforcement and ACID transactions are important Delta Lake capabilities, but they are not the best differentiator "over classic enterprise data warehouse solutions," because enterprise warehouses also typically provide managed reliability and transactional behavior. The unique lakehouse advantage here is open-source/open-format interoperability. References: Databricks Delta Lake and lakehouse architecture documentation.


NEW QUESTION # 21
What is used as a compute resource for Databricks SQL?

Answer: A


NEW QUESTION # 22
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?

Answer: C

Explanation:
Databricks SQL allows you to create multiple visualizations from a single query result. These visualizations can be customized independently and each can be added to a dashboard. This feature is explicitly supported and recommended in Databricks' documentation on dashboards and visualization workflows, enabling flexible reporting without duplicating queries.


NEW QUESTION # 23
Which of the following benefits of using Databricks SQL is provided by Data Explorer?

Answer: B

Explanation:
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. References: Discover and manage data using Data Explorer


NEW QUESTION # 24
A data analyst has been asked to use the below table sales_table to get the percentage rank of products within region by the sales:

The result of the query should look like this:

Which of the following queries will accomplish this task?
A)

B)

C)

Answer: B

Explanation:
The correct query to get the percentage rank of products within region by the sales is option B. This query uses the PERCENT_RANK() window function to calculate the relative rank of each product within each region based on the sales amount. The window function is partitioned by region and ordered by sales in descending order. The result is aliased as rank and displayed along with the region and product columns. The other options are incorrect because:
A) Option A uses the RANK() window function instead of the PERCENT_RANK() function. The RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
C) Option C uses the DENSE_RANK() window function instead of the PERCENT_RANK() function. The DENSE_RANK() function returns the rank of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM().
D) Option D uses the ROW_NUMBER() window function instead of the PERCENT_RANK() function. The ROW_NUMBER() function returns the sequential number of each row within the partition, but not the percentage rank. Also, the query does not have a GROUP BY clause, which is required for aggregate functions like SUM(). Reference:
1: PERCENT_RANK (Transact-SQL)
2: Window functions in Databricks SQL
3: Databricks Certified Data Analyst Associate Exam Guide


NEW QUESTION # 25
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