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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	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	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 5	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q55-Q60):

NEW QUESTION # 55
A BI analyst is building an analytical data model in Databricks using Delta Lake tables. The source system contains transactional sales data that changes frequently. The analyst chooses to apply the Data Vault 2.0 methodology to manage historical changes while ensuring scalability and auditability across multiple business domains.
Which component is used to capture the many-to-many relationship between hubs in a Data Vault v2 model?

A. Link Table
B. Satellite Table
C. Hub Table
D. Reference Table

Answer: A

Explanation:
Option C is correct. In Data Vault modeling, hubs represent core business entities, satellites store descriptive and historical attributes, and links represent relationships between hubs. A many-to-many relationship between business entities is therefore captured by a Link Table. The Databricks Data Analyst Associate Exam Guide includes Data Vault schemas as part of data modeling with Databricks SQL, and Databricks' Data Vault guidance explains that links represent relationships between hub entities. References: Databricks Data Analyst Associate Exam Guide and Databricks Data Vault guidance.

NEW QUESTION # 56
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. They will need to alter the Query to return two separate sets of results.
B. They will need to create two separate dashboards.
C. They will need to copy the Query and create one data visualization per query.
D. They will need to decide on a single data visualization to add to the dashboard.
E. They will need to add two separate visualizations to the dashboard based on the same Query.

Answer: E

Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. Reference: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

NEW QUESTION # 57
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

A. When another task needs to be replaced by the new task.
B. When another task has the same dependency libraries as the new task.
C. When another task needs to successfully complete before the new task begins.
D. When another task needs to use as little compute resources as possible.
E. When another task needs to fail before the new task begins.

Answer: C

Explanation:
Option E is correct. The Depends On field is used to define task dependencies in a Databricks job, so a downstream task waits for an upstream task according to the dependency rules. The standard use case is when one task must complete successfully before the next task starts. Official Databricks extract: "You can control the execution order of tasks by specifying dependencies between them," and Databricks runs upstream tasks before downstream tasks.

NEW QUESTION # 58
An analyst writes a query that contains a query parameter. They then add an area chart visualization to the query. While adding the area chart visualization to a dashboard, the analyst chooses " Dashboard Parameter " for the query parameter associated with the area chart.
Which of the following statements is true?

A. The area chart will use whatever is selected in the Dashboard Parameter while all or the other visualizations will remain changed regardless of their parameter use.
B. The area chart will use whatever value is input by the analyst when the visualization is added to the dashboard. The parameter cannot be changed by the user afterwards.
C. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.
D. The area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter.
E. The area chart will convert to a Dashboard Parameter.

Answer: D

Explanation:
A Dashboard Parameter is a parameter that is configured for one or more visualizations within a dashboard and appears at the top of the dashboard. The parameter values specified for a Dashboard Parameter apply to all visualizations reusing that particular Dashboard Parameter1. Therefore, if the analyst chooses "Dashboard Parameter" for the query parameter associated with the area chart, the area chart will use whatever is selected in the Dashboard Parameter along with all of the other visualizations in the dashboard that use the same parameter. This allows the user to filter the data across multiple visualizations using a single parameter widget2. References: Databricks SQL dashboards, Query parameters

NEW QUESTION # 59
Where can an admin or data owner grant database, table, and view permissions to a group?

A. Dashboard
B. SQL Warehouses
C. Settings
D. Data

Answer: D

Explanation:
Option B is correct. In the older Databricks SQL UI terminology used by this question, permissions for databases, tables, and views are managed from the Data area. In the current Databricks UI, this is handled through Catalog Explorer, where an admin or data owner opens the object, goes to the Permissions tab, clicks Grant, selects users or groups, and grants the required privileges. Dashboards are for reporting, SQL Warehouses provide compute, and Settings is not the object-level permission management area. Official Databricks documentation says privileges can be managed using SQL commands, CLI, Terraform, or Catalog Explorer, and the UI flow is: open the table details page, go to Permissions, click Grant, select users or groups, and assign privileges.

NEW QUESTION # 60
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