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

Topic	Details
Topic 1	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 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 contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
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.

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

NEW QUESTION # 22
A data analyst runs the following command:
INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers;
What is the result of running this command?

A. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, including any duplicate data.
B. The suppliers table now contains only the data from the new suppliers table.
C. The command fails because it is written incorrectly.
D. The suppliers table now contains both the data it had before the command was run and the data from the new suppliers table, and any duplicate data is deleted.
E. The suppliers table now contains the data from the new suppliers table, and the new suppliers table now contains the data from the suppliers table.

Answer: C

Explanation:
The command INSERT INTO stakeholders.suppliers TABLE stakeholders.new_suppliers is not a valid syntax for inserting data into a table in Databricks SQL. According to the documentation12, the correct syntax for inserting data into a table is either:
INSERT { OVERWRITE | INTO } [ TABLE ] table_name [ PARTITION clause ] [ ( column_name [, ...] ) | BY NAME ] query INSERT INTO [ TABLE ] table_name REPLACE WHERE predicate query The command in the question is missing the OVERWRITE or INTO keyword, and the query part that specifies the source of the data to be inserted. The TABLE keyword is optional and can be omitted. The PARTITION clause and the column list are also optional and depend on the table schema and the data source. Therefore, the command in the question will fail with a syntax error.
Reference:
INSERT | Databricks on AWS
INSERT - Azure Databricks - Databricks SQL | Microsoft Learn

NEW QUESTION # 23
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. Alerts don't work with queries that access tables.
B. Queries that use query parameters cannot be used with Alerts.
C. The wrong query parameter is being used. Alerts only work with Date and Time query parameters.
D. Queries that return results based on dates cannot be used with Alerts.
E. The wrong query parameter is being used. Alerts only work with drogdown list query parameters, not dates.

Answer: B

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 # 24
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 via a connection between Databricks and the desktop facilitated by Partner Connect.
B. Create a table by uploading the file using the Create page within Databricks SQL
C. Create a table by uploading the file to cloud storage and then importing the data to Databricks.
D. Create a table by manually copying and pasting the data values into cloud storage and then importing the data to Databricks.

Answer: B

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.

NEW QUESTION # 25
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

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

Answer: D

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. Reference:
How to Use Databricks to Encrypt and Protect PII Data
Automating Sensitive Data (PII/PHI) Detection
Databricks Certified Data Analyst Associate

NEW QUESTION # 26
A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform.
Which of the following projects should be completed in Databricks SQL?

A. Automating complex notebook-based workflows with multiple tasks
B. Segmenting customers into like groups using a clustering algorithm
C. Tracking usage of feature variables for machine learning projects
D. Testing the quality of data as it is imported from a source
E. Combining two data sources into a single, comprehensive dataset

Answer: E

Explanation:
Databricks SQL is a service that allows users to query data in the lakehouse using SQL and create visualizations and dashboards1. One of the common use cases for Databricks SQL is to combine data from different sources and formats into a single, comprehensive dataset that can be used for further analysis or reporting2. For example, a data analyst can use Databricks SQL to join data from a CSV file and a Parquet file, or from a Delta table and a JDBC table, and create a new table or view that contains the combined data3. This can help simplify the data management and governance, as well as improve the data quality and consistency. Reference:
Databricks SQL overview
Databricks SQL use cases
Joining data sources

NEW QUESTION # 27
......

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