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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	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	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 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 (Q22-Q27):

NEW QUESTION # 22
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. All files with the .customers extension are deleted.
B. The accounts.customers table is removed from the metastore, but the underlying data files are untouched.
C. The accounts.customers table is removed from the metastore, and the underlying data files are deleted.
D. Running SELECT * FROM delta. `dbfs:/stakeholders/customers` results in an error.
E. Running SELECT * FROM accounts.customers will return all rows in the table.

Answer: B

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 # 23
Which of the following layers of the medallion architecture is most commonly used by data analysts?

A. Bronze
B. All of these layers are used equally by data analysts
C. None of these layers are used by data analysts
D. Gold
E. Silver

Answer: D

Explanation:
The gold layer of the medallion architecture contains data that is highly refined and aggregated, and powers analytics, machine learning, and production applications. Data analysts typically use the gold layer to access data that has been transformed into knowledge, rather than just information. The gold layer represents the final stage of data quality and optimization in the lakehouse. Reference: What is the medallion lakehouse architecture?

NEW QUESTION # 24
Which statement about subqueries is correct?

A. Subqueries can retrieve data without requiring the creation of a table or view.
B. Subqueries can be used like other built-in functions to transform data into different data types.
C. Subqueries are not available in Databricks SQL
D. Subqueries can be used like other user-defined functions to transform data into different data types.

Answer: A

Explanation:
In Databricks SQL, a subquery is a nested query within a larger SQL query that allows for the retrieval of data without the necessity of creating a table or view. This is particularly useful for simplifying complex queries by breaking them down into more manageable parts. Subqueries can be employed in various clauses such as SELECT, FROM, and WHERE to perform operations like filtering, transforming, and aggregating data on-the-fly. This flexibility enhances query efficiency and readability without the overhead of persisting intermediate results as separate tables or views.

NEW QUESTION # 25
Which of the following statements about a refresh schedule is incorrect?

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

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 # 26
Data professionals with varying titles use the Databricks SQL service as the primary touchpoint with the Databricks Lakehouse Platform. However, some users will use other services like Databricks Machine Learning or Databricks Data Science and Engineering.
Which of the following roles uses Databricks SQL as a secondary service while primarily using one of the other services?

A. Data engineer
B. SQL analyst
C. Business intelligence analyst
D. Data analyst
E. Business analyst

Answer: A

Explanation:
Data engineers are primarily responsible for building, managing, and optimizing data pipelines and architectures. They use Databricks Data Science and Engineering service to perform tasks such as data ingestion, transformation, quality, and governance. Data engineers may use Databricks SQL as a secondary service to query, analyze, and visualize data from the lakehouse, but this is not their main focus. Reference: Databricks SQL overview, Databricks Data Science and Engineering overview, Data engineering with Databricks

NEW QUESTION # 27
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