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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	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	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 4	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 5	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
The stakeholders.customers table has 15 columns and 3,000 rows of data. The following command is run:

After running SELECT * FROM stakeholders.eur_customers, 15 rows are returned. After the command executes completely, the user logs out of Databricks.
After logging back in two days later, what is the status of the stakeholders.eur_customers view?

A. The view remains available but attempting to SELECT from it results in an empty result set because data in views are automatically deleted after logging out.
B. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
C. The view has been converted into a table.
D. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.
E. The view has been dropped.

Answer: E

Explanation:
The command you sent creates a TEMP VIEW, which is a type of view that is only visible and accessible to the session that created it. When the session ends or the user logs out, the TEMP VIEW is automatically dropped and cannot be queried anymore. Therefore, after logging back in two days later, the status of the stakeholders.eur_customers view is that it has been dropped and SELECT * FROM stakeholders.eur_customers will result in an error. The other options are not correct because:
A) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out.
C) The view is not available in the metastore, as it is a TEMP VIEW that is not registered in the metastore. The underlying data cannot be accessed with SELECT * FROM delta. stakeholders.eur_customers, as this is not a valid syntax for querying a Delta Lake table. The correct syntax would be SELECT * FROM delta.dbfs:/stakeholders/eur_customers, where the location path is enclosed in backticks. However, this would also result in an error, as the TEMP VIEW does not write any data to the file system and the location path does not exist.
D) The view does not remain available, as it is a TEMP VIEW that is dropped when the session ends or the user logs out. Data in views are not automatically deleted after logging out, as views do not store any data. They are only logical representations of queries on base tables or other views.
E) The view has not been converted into a table, as there is no automatic conversion between views and tables in Databricks. To create a table from a view, you need to use a CREATE TABLE AS statement or a similar command. Reference: CREATE VIEW | Databricks on AWS, Solved: How do temp views actually work? - Databricks - 20136, temp tables in Databricks - Databricks - 44012, Temporary View in Databricks - BIG DATA PROGRAMMERS, Solved: What is the difference between a Temporary View an ...

NEW QUESTION # 47
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

A. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.
B. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
C. They could redesign the data model to separate the data used in the final query into a new table.
D. They could only run the entire program on Sundays.
E. They could submit a feature request with Databricks to add this functionality.

Answer: B

Explanation:
Option B is correct. The program can be scheduled to run daily as a Databricks job, and Python control flow can determine whether the final query runs only when the current day is Sunday. Databricks jobs support scheduled execution, and PySpark notebooks can run SQL through Spark while using Python logic around those commands. Official Databricks extract: jobs can be configured "to run it on a time-based schedule," and spark.sql/Spark DataFrame operations allow Python-based workflows to interact with SQL data.

NEW QUESTION # 48
A data analyst needs to share a Databricks SQL dashboard with stakeholders that are not permitted to have accounts in the Databricks deployment. The stakeholders need to be notified every time the dashboard is refreshed.
Which approach can the data analyst use to accomplish this task with minimal effort/

A. By granting the stakeholders ' email addresses to the SQL Warehouse (formerly known as endpoint) subscribers list
B. By adding the stakeholders ' email addresses to the refresh schedule subscribers list
C. By downloading the dashboard as a PDF and emailing it to the stakeholders each time it is refreshed
D. By granting the stakeholders ' email addresses permissions to the dashboard

Answer: B

Explanation:
To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders ' email addresses to the dashboard ' s refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.
Reference: Manage scheduled dashboard updates and subscriptions

NEW QUESTION # 49
Which of the following data workloads will utilize a Gold table as its source?

A. A job that enriches data by parsing its timestamps into a human-readable format.
B. A job that ingests raw data from a streaming source into the Lakehouse.
C. A job that aggregates uncleaned data to create standard summary statistics.
D. A job that cleans data by removing malformatted records.
E. A job that queries aggregated data designed to feed into a dashboard.

Answer: E

Explanation:
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer "consists of aggregated data tailored for analytics and reporting" and is optimized for
"queries and dashboards."

NEW QUESTION # 50
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:
Which query did the analyst execute in order to get this result?

Answer: B

NEW QUESTION # 51
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