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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	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	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 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 (Q27-Q32):

NEW QUESTION # 27
A data analyst is attempting to drop a table my_table. The analyst wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table;
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

A. The table's data was smaller than 10 GB
B. The table did not have a location
C. The table was managed
D. The table's data was larger than 10 GB
E. The table was external

Answer: E

Explanation:
An external table is a table that is defined in the metastore, but its data is stored outside of the Databricks environment, such as in S3, ADLS, or GCS. When an external table is dropped, only the metadata is deleted from the metastore, but the data files are not affected. This is different from a managed table, which is a table whose data is stored in the Databricks environment, and whose data files are deleted when the table is dropped. To delete the data files of an external table, the analyst needs to specify the PURGE option in the DROP TABLE command, or manually delete the files from the storage system. Reference: DROP TABLE, Drop Delta table features, Best practices for dropping a managed Delta Lake table

NEW QUESTION # 28
A stakeholder has provided a data analyst with a lookup dataset in the form of a 50-row CSV file. The data analyst needs to upload this dataset for use as a table in Databricks SQL.
Which approach should the data analyst use to quickly upload the file into a table for use in Databricks SOL?

A. Create a table by uploading the file using the Create page within Databricks SQL
B. Create a table by uploading the file to cloud storage and then importing the data to Databricks.
C. Create a table via a connection between Databricks and the desktop facilitated by Partner Connect.
D. Create a table by manually copying and pasting the data values into cloud storage and then importing the data to Databricks.

Answer: A

Explanation:
Databricks provides a user-friendly interface that allows data analysts to quickly upload small datasets, such as a 50-row CSV file, and create tables within Databricks SQL. The steps are as follows:
* Access the Data Upload Interface:
* In the Databricks workspace, navigate to the sidebar and click on New > Add or upload data.
* Select Create or modify a table.
* Upload the CSV File:
* Click on the browse button or drag and drop the CSV file directly onto the designated area.
* The interface supports uploading up to 10 files simultaneously, with a total size limit of 2 GB.
* Configure Table Settings:
* After uploading, a preview of the data is displayed.
* Specify the table name, select the appropriate schema, and configure any additional settings as needed.
* Create the Table:
* Once all configurations are set, click on the Create Table button to finalize the process.
This method is efficient for quickly importing small datasets without the need for additional tools or complex configurations. Options B, C, and D involve more complex or manual processes that are unnecessary for this task.
Reference: Create or modify a table using file upload

NEW QUESTION # 29
A data analyst has been asked to configure an alert for a query that returns the income in the accounts_receivable table for a date range. The date range is configurable using a Date query parameter.
The Alert does not work.
Which of the following describes why the Alert does not work?

A. Queries that use query parameters cannot be used with Alerts.
B. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.
C. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
D. Alerts don't work with queries that access tables.
E. Queries that return results based on dates cannot be used with Alerts.

Answer: A

Explanation:
According to the Databricks documentation1, queries that use query parameters cannot be used with Alerts. This is because Alerts do not support user input or dynamic values. Alerts leverage queries with parameters using the default value specified in the SQL editor for each parameter. Therefore, if the query uses a Date query parameter, the alert will always use the same date range as the default value, regardless of the actual date. This may cause the alert to not work as expected, or to not trigger at all. Reference:
Databricks SQL alerts: This is the official documentation for Databricks SQL alerts, where you can find information about how to create, configure, and monitor alerts, as well as the limitations and best practices for using alerts.

NEW QUESTION # 30
Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain a less refined view of data than Silver tables.
B. Gold tables are more likely to contain aggregations than Silver tables.
C. Gold tables are more likely to contain truthful data than Silver tables.
D. Gold tables are more likely to contain more data than Silver tables.
E. Gold tables are more likely to contain valuable data than Silver tables.

Answer: B

Explanation:
Option A is correct. Silver tables are cleaned, validated, and enriched versions of data, often still retaining detailed records. Gold tables are typically business-ready, analytics-focused, and more likely to contain aggregations, dimensional models, and reporting-ready metrics. Official Databricks extract: Silver is associated with "Data cleaning and validation," while Gold is associated with "Dimensional modeling and aggregation." Databricks also states that Gold data is often highly aggregated and tailored for analytics and reporting.

NEW QUESTION # 31
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. Data enhancement
B. Last-mile ETL
C. Data blending
D. Last-mile
E. Data testing

Answer: B

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 # 32
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