Databricks-Certified-Data-Analyst-Associate Test Cram - Databricks-Certified-Data-Analyst-Associate Exam Pass Guide

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Databricks Databricks-Certified-Data-Analyst-Associate Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Data Analyst Associate Exam
Exam Number:	Databricks-Certified-Data-Analyst-Associate
Related Certifications:	Databricks Certified Machine Learning Associate Databricks Certified Data Engineer Associate
Passing Score:	70%
Available Languages:	English
Exam Price:	$200 USD
Certificate Validity Period:	2 years
Real Exam Qty:	45
Exam Duration:	90 minutes
Exam Format:	Multiple Choice
Sample Questions:	Databricks Databricks-Certified-Data-Analyst-Associate Sample Questions
Exam Way:	Online proctored or test center proctored
Pre Condition:	No formal prerequisites. Databricks recommends 6+ months of hands-on experience with data analysis and Databricks SQL.
Official Syllabus URL:	https://www.databricks.com/learn/certification/data-analyst-associate

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

Topic	Details
Topic 1	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.
Topic 2	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 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 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.

Databricks Certified Data Analyst Associate Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
Which of the following commands will return the location of database customer360?

A. DESCRIBE LOCATION customer360;
B. DROP DATABASE customer360;
C. DESCRIBE DATABASE customer360;
D. USE DATABASE customer360;
E. ALTER DATABASE customer360 SET DBPROPERTIES ( ' location ' = ' /user ' );

Answer: C

Explanation:
Option C is correct. DESCRIBE DATABASE customer360 returns metadata for the database/schema, including its filesystem location. DESCRIBE LOCATION is for external locations, not databases. DROP DATABASE deletes a database, ALTER DATABASE changes metadata/properties, and USE DATABASE changes the current database context. Official Databricks extract: DESCRIBE DATABASE is an alias for DESCRIBE SCHEMA, and DESCRIBE SCHEMA returns metadata including the schema's "location on the filesystem."

NEW QUESTION # 93
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
B. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
C. The suppliers table now contains only the data from the new suppliers table.
D. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
E. The command fails because it is written incorrectly.

Answer: E

Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 94
Which of the following statements about a refresh schedule is incorrect?

A. A query being refreshed on a schedule does not use a SQL Warehouse (formerly known as SQL Endpoint).
B. Refresh schedules can be configured in the Query Editor.
C. A query can be refreshed anywhere from 1 minute lo 2 weeks
D. You must have workspace administrator privileges to configure a refresh schedule
E. A refresh schedule is not the same as an alert.

Answer: D

Explanation:
This statement is incorrect. In Databricks SQL, any user with sufficient permissions on the query or dashboard can configure a refresh schedule-workspace administrator privileges are not required.
Here is the breakdown of the correct information:
A . True - Queries can be scheduled to refresh at intervals ranging from 1 minute to 2 weeks.
B . True - You can configure refresh schedules in the Query Editor.
C . False statement - A query being refreshed does use a SQL Warehouse. However, the option in question says it does not use a warehouse, which would be incorrect in a different context. Since this is a trickier one, we know that scheduled queries do require a SQL Warehouse to run.
D . True - Refresh schedules are different from alerts; alerts are triggered based on specific conditions being met in query results.
E . False (and thus the correct answer to this question) - You do not need to be a workspace admin to set a refresh schedule. You only need the correct permissions on the object.

NEW QUESTION # 95
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?

A. Option CUPDATE suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;
B. Option BCOPY INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;
C. Option AMERGE INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;
D. Option DINSERT INTO suppliersUSING new_suppliersON suppliers.supplier_id = new_suppliers.
supplier_idWHEN NOT MATCHED THEN INSERT *;

Answer: C

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 # 96
A data analyst wants the following output:
customer_name
number_of_orders
John Doe
388
Zhang San
234
Which statement will produce this output?

A. SELECT customer_name, count(order_id) number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_id USE customer_name;
B. SELECT customer_name, count(order_id) AS number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_idGROUP BY customer_name;
C. SELECT customerjiame, count(order_id)FROM customersJOIN ordersON customers.customer_id = orders.customer_id GROUP BY customerjiame;
D. SELECT customerjiame, (order_id) number_of_ordersFROM customersJOIN ordersON customers.
customer_id = orders.customer_id;

Answer: B

Explanation:
To get the number of orders per customer, you need to join the customers and orders tables on the customer_id, count the order_id, and group the results by customer_name. The correct SQL syntax, as outlined in Databricks SQL documentation, is to use GROUP BY on the selected customer field and use COUNT for aggregation. Only option A does this correctly, while the other options contain syntax errors or incorrect field names.

NEW QUESTION # 97
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