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

Topic	Details
Topic 1	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 2	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 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	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	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 (Q117-Q122):

NEW QUESTION # 117
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?

A. A quantitative variable that can take on an uncountable set of values
B. A quantitative variable that never stops changing
C. A categorical variable in which the number of categories continues to increase over time
D. A quantitative variable Chat can take on a finite or countably infinite set of values

Answer: A

Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family).
Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.

NEW QUESTION # 118
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

A. Legal requirements for the area in which the analysis is being performed
B. All of these considerations
C. Legal requirements for the area in which the data was collected
D. None of these considerations
E. Organization-specific best practices for Pll data

Answer: B

Explanation:
Data analysts should consider all of these factors when working with PII data, as they may affect the data security, privacy, compliance, and quality. PII data is any information that can be used to identify a specific individual, such as name, address, phone number, email, social security number, etc. PII data may be subject to different legal and ethical obligations depending on the context and location of the data collection and analysis. For example, some countries or regions may have stricter data protection laws than others, such as the General Data Protection Regulation (GDPR) in the European Union. Data analysts should also follow the organization-specific best practices for PII data, such as encryption, anonymization, masking, access control, auditing, etc. These best practices can help prevent data breaches, unauthorized access, misuse, or loss of PII data. References:
* How to Use Databricks to Encrypt and Protect PII Data
* Automating Sensitive Data (PII/PHI) Detection
* Databricks Certified Data Analyst Associate

NEW QUESTION # 119
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 add two separate visualizations to the dashboard based on the same Query.
B. They will need to create two separate dashboards.
C. They will need to alter the Query to return two separate sets of results.
D. They will need to decide on a single data visualization to add to the dashboard.
E. They will need to copy the Query and create one data visualization per query.

Answer: A

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 # 120
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. Generate a Personal Access Token that is good for 1 day and share it with the client.
B. Take a screenshot of the dashboard and share it with the client.
C. Download the Dashboard as a PDF and share it with the client.
D. Download a PNG file of the visualizations in the dashboard and share them with the client.
E. Set a refresh schedule for the dashboard and enter the client ' s email address in the " Subscribers " box.

Answer: A

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 # 121
A managed table and an unmanaged external table were both created in Databricks SQL, and data was ingested into each table. Later, both tables were dropped.
What is the status of data for each of those tables?

A. The data in both tables were deleted.
B. The data in both tables were left untouched.
C. The data in the managed table were deleted, and the data in the unmanaged external table were left untouched.
D. The data in the unmanaged external table were deleted, and the data in the managed table were left untouched.

Answer: C

Explanation:
Option B is correct. In Databricks, when a managed table is dropped, Databricks removes the table and its associated managed storage location. For an external table, Databricks removes only the table metadata; the underlying external data files are not deleted. Therefore, dropping both tables deletes the managed table's data but leaves the external table's data untouched. Official Databricks documentation states that DROP TABLE
"deletes the table and removes the directory associated with the table from the file system if the table is not an EXTERNAL table," and for external tables, Unity Catalog "removes the table metadata but does not delete the underlying data files."

NEW QUESTION # 122
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