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

TopicDetails
Topic 1
  • 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 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
  • 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 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
  • 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.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q22-Q27):

NEW QUESTION # 22
A database was created in Databricks SQL using the following statement:
CREATE SCHEMA accounting LOCATION ' dbfs:/accounting/data ' ;
Where will data for this database be stored?

Answer: B

Explanation:
Option B is correct. The statement explicitly sets the schema/database location to dbfs:/accounting/data. In Databricks SQL, CREATE SCHEMA supports a LOCATION schema_directory, and the documentation explains that the schema directory is the filesystem path where the schema is created. Because the user supplied LOCATION ' dbfs:/accounting/data ' , Databricks uses that path instead of the default warehouse directory. Option D would apply only when no custom location is supplied. References: Databricks CREATE SCHEMA documentation.


NEW QUESTION # 23
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

Answer: B

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 # 24
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?

Answer: D

Explanation:
Option D is correct. Databricks SQL alerts run a query on a schedule, evaluate a condition, and notify destinations when the condition is met. Since the requirement is notification through a messaging webhook, the alert should use a webhook alert destination. Official Databricks extract: alerts "run queries on a schedule and notify you when a condition that you define is met." Databricks notification documentation also states that admins can configure alternate notification destinations using webhooks.


NEW QUESTION # 25
A data analyst created and is the owner of the managed table my_ table. They now want to change ownership of the table to a single other user using Data Explorer.
Which of the following approaches can the analyst use to complete the task?

Answer: A

Explanation:
The Owner field in the table page shows the current owner of the table and allows the owner to change it to another user or group. To change the ownership of the table, the owner can click on the Owner field and select the new owner from the drop-down list. This will transfer the ownership of the table to the selected user or group and remove the previous owner from the list of table access control entries1. The other options are incorrect because:
A . Removing the owner's account from the Owner field will not change the ownership of the table, but will make the table ownerless2.
B . Selecting All Users from the Owner field will not change the ownership of the table, but will grant all users access to the table3.
D . Selecting the Admins group from the Owner field will not change the ownership of the table, but will grant the Admins group access to the table3.
E . Removing all access from the Owner field will not change the ownership of the table, but will revoke all access to the table4. Reference:
1: Change table ownership
2: Ownerless tables
3: Table access control
4: Revoke access to a table


NEW QUESTION # 26
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

Answer: C

Explanation:
Option E is correct. In the current Databricks UI, Data Explorer is known as Catalog Explorer. It is the location used to browse catalogs, schemas, tables, and views, review object metadata, and request or inspect access-related information. DBFS, Jobs, Dashboards, and Repos are not the table-permission review location.
Official Databricks extract: users can navigate the catalog tree, open an object's page, and request additional privileges from Catalog Explorer.


NEW QUESTION # 27
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