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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	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	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 4	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 5	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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
A data analyst wants to create a Databricks SQL dashboard with multiple data visualizations and multiple counters. What must be completed before adding the data visualizations and counters to the dashboard?

A. A markdown-based tile must be added to the top of the dashboard displaying the dashboard ' s name.
B. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
C. All data visualizations and counters must be created using Queries.
D. A SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.

Answer: C

Explanation:
In Databricks SQL, when creating a dashboard that includes multiple data visualizations and counters, it is imperative that each visualization and counter is based on a query. The process involves the following steps:
* Develop Queries:
* For each desired visualization or counter, write a SQL query that retrieves the necessary data.
* Create Visualizations and Counters:
* After executing each query, utilize the results to create corresponding visualizations or counters.
Databricks SQL offers a variety of visualization types to represent data effectively.
* Assemble the Dashboard:
* Add the created visualizations and counters to your dashboard, arranging them as needed to convey the desired insights.
By ensuring that all components of the dashboard are derived from queries, you maintain consistency, accuracy, and the ability to refresh data as needed. This approach also facilitates easier maintenance and updates to the dashboard elements.
Reference: Visualization in Databricks SQL

NEW QUESTION # 32
A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?

A. Last-mile ETL
B. Data enhancement
C. Last-mile
D. Data testing
E. Data blending

Answer: A

Explanation:
Last-mile ETL is the term used to describe the additional processing of data that is done by data analysts or data scientists after the data has been ingested, transformed, and stored in the lakehouse by data engineers. Last-mile ETL typically involves tasks such as data cleansing, data enrichment, data aggregation, data filtering, or data sampling that are specific to the analysis or machine learning use case. Last-mile ETL can be done using Databricks SQL, Databricks notebooks, or Databricks Machine Learning. Reference: Databricks - Last-mile ETL, Databricks - Data Analysis with Databricks SQL

NEW QUESTION # 33
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

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

Answer: A

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 # 34
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can turn on the Auto Stop feature for the SQL endpoint.
B. They can reduce the cluster size of the SQL endpoint.
C. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
D. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
E. They can set up the dashboard's SQL endpoint to be serverless.

Answer: A

Explanation:
Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse.
Serverless can help with management, but the feature directly matching "only running when necessary" is Auto Stop. Official Databricks extract: "Auto Stop determines whether the warehouse stops if it's idle for the specified number of minutes." Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

NEW QUESTION # 35
The stakeholders.customers table has 15 columns and 3,000 rows of dat
a. 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 has been converted into a table.
B. The view has been dropped.
C. 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.
D. The view remains available and SELECT * FROM stakeholders.eur_customers will execute correctly.
E. The view is not available in the metastore, but the underlying data can be accessed with SELECT * FROM delta. `stakeholders.eur_customers`.

Answer: D

Explanation:
In Databricks, a view is a saved SQL query definition that references existing tables or other views. Once created, a view remains persisted in the metastore (such as Unity Catalog or Hive Metastore) until it is explicitly dropped.
Key points:
Views do not store data themselves but reference data from underlying tables.
Logging out or being inactive does not delete or alter views.
Unless a user or admin explicitly drops the view or the underlying data/table is deleted, the view continues to function as expected.
Therefore, after logging back in-even days later-a user can still run SELECT * FROM stakeholders.eur_customers, and it will return the same data (provided the underlying table hasn't changed).

NEW QUESTION # 36
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