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

Topic	Details
Topic 1	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 2	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 3	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 4	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 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 (Q75-Q80):

NEW QUESTION # 75
Which of the following statements about a refresh schedule is incorrect?

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

Answer: B

Explanation:
Refresh schedules are used to rerun queries at specified intervals, and these queries typically require computational resources to execute. In the context of a cloud data service like Databricks, this would typically involve the use of a SQL Warehouse (or a SQL Endpoint, as they were formerly known) to provide the necessary computational resources. Therefore, the statement is incorrect because scheduled query refreshes would indeed use a SQL Warehouse/Endpoint to execute the query.

NEW QUESTION # 76
A table named user_ltv is being used to create a view that will be used by data analysts on various teams.
Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:
CREATE VIEW user_ltv_no_minors AS
SELECT email, age, ltv
FROM user_ltv
WHERE
CASE
WHEN is_member( " auditing " ) THEN TRUE
ELSE age > = 18
END;
An analyst who is not a member of the auditing group executes the following query:
SELECT * FROM user_ltv_no_minors;
Which statement describes the results returned by this query?

A. All values for the age column will be returned as null values, all other columns will be returned with the values in user_ltv.
B. All records from all columns will be displayed with the values in user_ltv.
C. All columns will be displayed normally for those records that have an age greater than 17; records not meeting this condition will be omitted.
D. All columns will be displayed normally for those records that have an age greater than 18; records not meeting this condition will be omitted.
E. All age values less than 18 will be returned as null values, all other columns will be returned with the values in user_ltv.

Answer: C

Explanation:
Option A is correct. The user is not a member of the auditing group, so is_member( " auditing " ) evaluates to false and the ELSE age > = 18 branch controls the filter. Rows with age > = 18 are returned; rows under 18 are omitted. "Age greater than 17" is equivalent to age > = 18 for integer ages. Official Databricks extract:
is_member() "returns TRUE if the current user is a member" of the specified group, and Databricks describes dynamic views as views that can filter rows based on group membership.

NEW QUESTION # 77
Data professionals with varying responsibilities use the Databricks Lakehouse Platform Which role in the Databricks Lakehouse Platform use Databricks SQL as their primary service?

A. Business analyst
B. Data engineer
C. Platform architect
D. Data scientist

Answer: A

Explanation:
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main service. Databricks SQL provides an environment tailored for executing SQL queries, creating visualizations, and developing dashboards, which aligns with the typical responsibilities of business analysts who focus on interpreting data to inform business decisions. While data scientists and data engineers also interact with the Databricks platform, their primary tools and services differ; data scientists often engage with machine learning frameworks and notebooks, whereas data engineers focus on data pipelines and ETL processes.
Platform architects are involved in designing and overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed, business analysts are the primary users of Databricks SQL.
Reference: The scope of the lakehouse platform

NEW QUESTION # 78
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 customerjiame, (order_id) number_of_ordersFROM customersJOIN ordersON customers.
customer_id = orders.customer_id;
B. SELECT customerjiame, count(order_id)FROM customersJOIN ordersON customers.customer_id = orders.customer_id GROUP BY customerjiame;
C. SELECT customer_name, count(order_id) AS number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_idGROUP BY customer_name;
D. SELECT customer_name, count(order_id) number_of_ordersFROM customersJOIN ordersON customers.customer_id = orders.customer_id USE customer_name;

Answer: C

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 # 79
A data analyst needs to use the Databricks Lakehouse Platform to quickly create SQL queries and data visualizations. It is a requirement that the compute resources in the platform can be made serverless, and it is expected that data visualizations can be placed within a dashboard.
Which of the following Databricks Lakehouse Platform services/capabilities meets all of these requirements?

A. Databricks Notebooks
B. Delta Lake
C. Databricks Machine Learning
D. Databricks SQL
E. Tableau

Answer: D

Explanation:
Databricks SQL is a serverless data warehouse on the Lakehouse that lets you run all of your SQL and BI applications at scale with your tools of choice, all at a fraction of the cost of traditional cloud data warehouses1. Databricks SQL allows you to create SQL queries and data visualizations using the SQL Analytics UI or the Databricks SQL CLI2. You can also place your data visualizations within a dashboard and share it with other users in your organization3. Databricks SQL is powered by Delta Lake, which provides reliability, performance, and governance for your data lake4. References:
* Databricks SQL
* Query data using SQL Analytics
* Visualizations in Databricks notebooks
* Delta Lake

NEW QUESTION # 80
......

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