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

SectionWeightObjectives
Securing Data8%- Security and Access Control
  • 1. Data Access Policies
  • 2. Permissions and Roles
  • 3. Unity Catalog Security
Executing Queries Using Databricks SQL and SQL Warehouses20%- SQL Querying
  • 1. Query Optimization
  • 2. Writing SQL Queries
  • 3. SQL Warehouses
  • 4. Filtering and Aggregation
  • 5. Joins and Subqueries
Creating Dashboards and Visualizations16%- Visualization
  • 1. Dashboards
  • 2. Sharing and Publishing
  • 3. Charts and Graphs
  • 4. Alerts and Filters
Analyzing Queries15%- Query Analysis
  • 1. Reading Query Plans
  • 2. Performance Tuning
  • 3. Monitoring Query Execution
Importing Data5%- Data Import
  • 1. Loading CSV and JSON Data
  • 2. Uploading Files
  • 3. Creating Tables from Imported Data
Managing Data8%- Data Management
  • 1. Cleaning Data with SQL
  • 2. Tagging and Lineage
  • 3. Handling Missing and Invalid Data
  • 4. Discovering Data Assets
Data Modeling with Databricks SQL5%- Modeling Concepts
  • 1. Fact and Dimension Tables
  • 2. Views and Relationships
  • 3. Data Organization
Developing AI/BI Genie Spaces12%- AI and BI Features
  • 1. Workspace Configuration
  • 2. AI/BI Genie
  • 3. Natural Language Queries
Understanding of Databricks Data Intelligence Platform11%- Catalog and Governance
  • 1. Catalogs and Schemas
  • 2. Data Lineage
  • 3. Views and Certified Tables
  • 4. Managed and External Tables
- Platform Components
  • 1. Mosaic AI
  • 2. Databricks SQL
  • 3. Delta Lake
  • 4. Unity Catalog
  • 5. Lakeflow Jobs

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
A data scientist has asked a data analyst to create histograms for every continuous variable in a data set. The data analyst needs to identify which columns are continuous in the data set.
What describes a continuous variable?

Answer: A

Explanation:
A continuous variable is a type of quantitative variable that can assume an infinite number of values within a given range. This means that between any two possible values, there can be an infinite number of other values. For example, variables such as height, weight, and temperature are continuous because they can be measured to any level of precision, and there are no gaps between possible values. This is in contrast to discrete variables, which can only take on specific, distinct values (e.g., the number of children in a family). Understanding the nature of continuous variables is crucial for data analysts, especially when selecting appropriate statistical methods and visualizations, such as histograms, to accurately represent and analyze the data.


NEW QUESTION # 74
A data analysis team is working with the table_bronze SQL table as a source for one of its most complex projects. A stakeholder of the project notices that some of the downstream data is duplicative. The analysis team identifies table_bronze as the source of the duplication.
Which of the following queries can be used to deduplicate the data from table_bronze and write it to a new table table_silver?
A)
CREATE TABLE table_silver AS
SELECT DISTINCT *
FROM table_bronze;
B)
CREATE TABLE table_silver AS
INSERT *
FROM table_bronze;
C)
CREATE TABLE table_silver AS
MERGE DEDUPLICATE *
FROM table_bronze;
D)
INSERT INTO TABLE table_silver
SELECT * FROM table_bronze;
E)
INSERT OVERWRITE TABLE table_silver
SELECT * FROM table_bronze;

Answer: C

Explanation:
Option A uses the SELECT DISTINCT statement to remove duplicate rows from the table_bronze and create a new table table_silver with the deduplicated data. This is the correct way to deduplicate data using Spark SQL12. Option B simply inserts all the rows from table_bronze into table_silver, without removing any duplicates. Option C is not a valid syntax for Spark SQL, as there is no MERGE DEDUPLICATE statement. Option D appends all the rows from table_bronze into table_silver, without removing any duplicates. Option E overwrites the existing data in table_silver with the data from table_bronze, without removing any duplicates. Reference: Delete Duplicate using SPARK SQL, Spark SQL - How to Remove Duplicate Rows


NEW QUESTION # 75
A data analyst has created a Query in Databricks SQL, and now wants to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which step will the data analyst need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

Answer: D


NEW QUESTION # 76
A data analyst needs to share a Databricks SQL dashboard with stakeholders that are not permitted to have accounts in the Databricks deployment. The stakeholders need to be notified every time the dashboard is refreshed.
Which approach can the data analyst use to accomplish this task with minimal effort/

Answer: D

Explanation:
To share a Databricks SQL dashboard with stakeholders who do not have accounts in the Databricks deployment and ensure they are notified upon each refresh, the data analyst can add the stakeholders ' email addresses to the dashboard ' s refresh schedule subscribers list. This approach allows the stakeholders to receive email notifications containing the latest dashboard updates without requiring them to have direct access to the Databricks workspace. This method is efficient and minimizes effort, as it automates the notification process and ensures stakeholders remain informed of the most recent data insights.
Reference: Manage scheduled dashboard updates and subscriptions


NEW QUESTION # 77
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?

Answer: C

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. References: DROP TABLE | Databricks on AWS, Best practices for dropping a managed Delta Lake table - Databricks


NEW QUESTION # 78
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