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

Topic	Details
Topic 1	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 2	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 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	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 (Q76-Q81):

NEW QUESTION # 76
What does Partner Connect do when connecting Power Bl and Tableau?

A. Creates a Personal Access Token for authentication into Databricks SQL and emails it to you.
B. Downloads a configuration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as a SQL Endpoint).
C. Creates a Personal Access Token. downloads and installs an ODBC driver, and downloads a configuration file for connection by Power Bl or Tableau to a SQL Warehouse (formerly known as a SQL Endpoint).
D. Downloads and installs an ODBC driver.

Answer: C

Explanation:
When connecting Power BI and Tableau through Databricks Partner Connect, the system automates several steps to streamline the integration process:
* Personal Access Token Creation: Partner Connect generates a Databricks personal access token, which is essential for authenticating and establishing a secure connection between Databricks and the BI tools.
* ODBC Driver Installation: The appropriate ODBC driver is downloaded and installed. This driver facilitates communication between the BI tools and Databricks, ensuring compatibility and optimal performance.
* Configuration File Download: A configuration file tailored for the selected BI tool (Power BI or Tableau) is provided. This file contains the necessary connection details, simplifying the setup process within the BI tool.
By automating these steps, Partner Connect ensures a seamless and efficient integration, reducing manual configuration efforts and potential errors.
Reference: Connect Tableau and Databricks

NEW QUESTION # 77
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Ganglia
B. Cluster Event Log
C. Driver's log file
D. Workspace audit logs
E. Executor's log file

Answer: B

Explanation:
Option D is correct. Cluster resizing caused by autoscaling is recorded as a cluster activity event, so the administrator should review the Cluster Event Log. Driver and executor logs are useful for application
/runtime troubleshooting, not for the cluster resizing timeline. Ganglia shows metrics but is not the event timeline. Official Databricks documentation identifies cluster activity events as the place to retrieve cluster activity, and Databricks autoscaling documentation describes resize events such as "Cluster resize request started."

NEW QUESTION # 78
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 convert to a Dashboard Parameter.
B. 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.
C. 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.
D. The area chart will use whatever value is chosen on the dashboard at the time the area chart is added to the dashboard.
E. 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.

Answer: B

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. Reference: Databricks SQL dashboards, Query parameters

NEW QUESTION # 79
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

A. They can increase the cluster size of the SQL endpoint.
B. They can turn on the Serverless feature for the SQL endpoint.
C. They can increase the maximum bound of the SQL endpoint's scaling range.
D. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized."
E. They can turn on the Auto Stop feature for the SQL endpoint.

Answer: C

Explanation:
Option B is correct. The problem is many users running small queries simultaneously, which is a concurrency issue. Increasing the maximum number of clusters lets the SQL warehouse scale out to serve more concurrent queries. Increasing cluster size is more useful for complex or resource-heavy queries, not necessarily many small simultaneous queries. Auto Stop reduces cost but does not improve active-query latency. Official Databricks extract: "You can increase the maximum clusters if you want to handle more concurrent users," and Databricks recommends monitoring queued queries and adjusting maximum clusters.

NEW QUESTION # 80
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. The data analyst needs to upload this dataset for use as a table in Databricks SQL.
Which approach should the data analyst use to quickly upload the file into a table for use in Databricks SOL?

A. Create a table by uploading the file to cloud storage and then importing the data to Databricks.
B. Create a table by manually copying and pasting the data values into cloud storage and then importing the data to Databricks.
C. Create a table via a connection between Databricks and the desktop facilitated by Partner Connect.
D. Create a table by uploading the file using the Create page within Databricks SQL

Answer: D

Explanation:
Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:
Access the Data Upload Interface:
In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
Select Create or modify a table.
Upload the CSV File:
Click on the browse button or drag and drop the CSV file directly onto the designated area.
The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.
Configure Table Settings:
After uploading, a preview of the data is displayed.
Specify the table name, select the appropriate schema, and configure any additional settings as needed.
Create the Table:
Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.

NEW QUESTION # 81
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