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

Topic	Details
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	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 3	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 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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q107-Q112):

NEW QUESTION # 107
A data analyst wants the following output:

Which statement will produce this output?

Answer: A

Explanation:
Option D is correct because the desired result needs one row per customer, so the query must aggregate orders by customer_name. It also needs the output column name number_of_orders, so the aggregate expression must be aliased. Databricks SQL documentation states that count "returns the number" of rows in a group, and the SELECT clause supports a column alias for an expression result. Therefore, count(order_id) AS number_of_orders with GROUP BY customer_name is the correct statement. Option B counts correctly but does not alias the output column as required. Option A does not aggregate. Option C uses invalid SQL syntax because USE customer_name is not a grouping clause. References: Databricks count aggregate function and SELECT clause documentation.

NEW QUESTION # 108
In a healthcare provider organization using Delta Lake to store electronic health records, a data analyst needs to analyze a snapshot of the patient_records table from two weeks ago before some recent data corrections were applied.
What approach should the Data Engineer take to allow the analyst to query that specific prior version?

A. Truncate the table to remove all data, then reload the data from two weeks ago into the truncated table for the analyst to query.
B. Use the VACUUM command to remove all versions of the table older than two weeks, then the analyst can query the remaining version.
C. Restore the table to the version from two weeks ago using the RESTORE command, and have the analyst query the restored table.
D. Identify the version number corresponding to two weeks ago from the Delta transaction log, share that version number with the analyst to query using VERSION AS OF syntax, or export that version to a new Delta table for the analyst to query.

Answer: D

Explanation:
Option B is correct. Delta Lake supports time travel, which allows users to query previous table versions by timestamp or version number. The version can be obtained from table history and queried using VERSION AS OF. Restoring the table would change the current table state for everyone, which is not necessary when the analyst only needs to analyze a historical snapshot. VACUUM would remove old data files and can prevent time travel. Official Databricks extract: time travel supports "querying previous table versions," and the syntax includes SELECT * FROM people10m VERSION AS OF 123. Databricks also states that the version can be obtained from DESCRIBE HISTORY. Note: the two-week snapshot is queryable only if the required log and data files are still retained.

NEW QUESTION # 109
A data analyst has been asked to provide a list of options on how to share a dashboard with a client. It is a security requirement that the client does not gain access to any other information, resources, or artifacts in the database.
Which of the following approaches cannot be used to share the dashboard and meet the security requirement?

A. Set a refresh schedule for the dashboard and enter the client ' s email address in the " Subscribers " box.
B. Take a screenshot of the dashboard and share it with the client.
C. Download a PNG file of the visualizations in the dashboard and share them with the client.
D. Download the Dashboard as a PDF and share it with the client.
E. Generate a Personal Access Token that is good for 1 day and share it with the client.

Answer: E

Explanation:
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

NEW QUESTION # 110
A data analyst is working in an organization that utilizes a multi-hop, medallion architecture. They have been tasked with creating a new Gold table from an existing Silver table.
Which query is performing a hop from a Silver table to a Gold table?

Answer: B

Explanation:
Option C is the correct answer because it creates a new aggregated table, store_sales, from the existing cleaned
/refined table cleaned_transactions. In a medallion architecture, a table such as cleaned_transactions represents a Silver-layer table because it contains cleaned or validated transaction-level data. The query then creates store_sales by grouping by store_id and calculating SUM(sales), which is an aggregation suitable for analytics and reporting. That is exactly the Silver-to-Gold hop.
Option A creates cleaned_transactions from transactions, so it is moving toward a cleaned Silver table, not creating a Gold table. Option B creates transactions from raw_transactions, which is closer to a raw-to-Bronze or Bronze preparation step. Option D filters rows from transactions into cleaned_transactions, which is also a cleaning/validation step associated with Silver, not Gold.
Exact extract from official Databricks documentation: Databricks describes medallion architecture as improving data through "Bronze # Silver # Gold layer tables." The same official page identifies Silver as
"Data cleaning and validation" and Gold as "Dimensional modeling and aggregation." Databricks also states that the Gold layer "consists of aggregated data tailored for analytics and reporting."

NEW QUESTION # 111
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 the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.
B. 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.
C. The suppliers table now contains only the data from the new suppliers table.
D. The command fails because it is written incorrectly.
E. 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.

Answer: D

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 # 112
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