Databricks-Certified-Data-Analyst-Associate Exam Duration | Reliable Databricks-Certified-Data-Analyst-Associate Test Notes

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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	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 contrast MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
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	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
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

A. It has increased customization capabilities
B. It is more performant than other SQL dialects
C. It is more compatible with Spark's interpreters
D. It allows for the use of Photon's computation optimizations
E. It is easy to migrate existing SQL queries to Databricks SQL

Answer: E

Explanation:
Databricks SQL uses ANSI SQL as its standard SQL dialect, which means it follows the SQL specifications defined by the American National Standards Institute (ANSI). This makes it easier to migrate existing SQL queries from other data warehouses or platforms that also use ANSI SQL or a similar dialect, such as PostgreSQL, Oracle, or Teradata. By using ANSI SQL, Databricks SQL avoids surprises in behavior or unfamiliar syntax that may arise from using a non-standard SQL dialect, such as Spark SQL or Hive SQL12. Moreover, Databricks SQL also adds compatibility features to support common SQL constructs that are widely used in other data warehouses, such as QUALIFY, FILTER, and user-defined functions2. Reference: ANSI compliance in Databricks Runtime, Evolution of the SQL language at Databricks: ANSI standard by default and easier migrations from data warehouses

NEW QUESTION # 32
After running DESCRIBE EXTENDED accounts.customers;, the following was returned:

Now, a data analyst runs the following command:
DROP accounts.customers;
Which of the following describes the result of running this command?

A. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
B. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
C. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
D. Running SELECT * FROM accounts.customers will return all rows in the table.
E. All files with the .customers extension are deleted.

Answer: A

Explanation:
the accounts.customers table is an EXTERNAL table, which means that it is stored outside the default warehouse directory and is not managed by Databricks. Therefore, when you run the DROP command on this table, it only removes the metadata information from the metastore, but does not delete the actual data files from the file system. This means that you can still access the data using the location path (dbfs:/stakeholders/customers) or create another table pointing to the same location. However, if you try to query the table using its name (accounts.customers), you will get an error because the table no longer exists in the metastore. Reference: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks

NEW QUESTION # 33
Which of the following statements about a refresh schedule is incorrect?

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

Answer: D

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 # 34
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 SQL warehouse (formerly known as SQL endpoint) must be turned on and selected.
B. All data visualizations and counters must be created using Queries.
C. The dashboard owner must also be the owner of the queries, data visualizations, and counters.
D. A markdown-based tile must be added to the top of the dashboard displaying the dashboard's name.

Answer: B

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.

NEW QUESTION # 35
Which of the following benefits of using Databricks SQL is provided by Data Explorer?

A. It can be used to run UPDATE queries to update any tables in a database.
B. It can be used to produce dashboards that allow data exploration.
C. It can be used to make visualizations that can be shared with stakeholders.
D. It can be used to connect to third party Bl cools.
E. It can be used to view metadata and data, as well as view/change permissions.

Answer: E

Explanation:
Data Explorer is a user interface that allows you to discover and manage data, schemas, tables, models, and permissions in Databricks SQL. You can use Data Explorer to view schema details, preview sample data, and see table and model details and properties. Administrators can view and change owners, and admins and data object owners can grant and revoke permissions1. Reference: Discover and manage data using Data Explorer

NEW QUESTION # 36
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Databricks-Certified-Data-Analyst-Associate Exam Duration | Reliable Databricks-Certified-Data-Analyst-Associate Test Notes

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

Reliable Databricks Databricks-Certified-Data-Analyst-Associate Test Notes | Mock Databricks-Certified-Data-Analyst-Associate Exams

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

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