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

Section	Weight	Objectives
Topic 1: Analyzing Queries	15%	- Query history and auditing - Execution plans and analysis - Performance optimization - Liquid clustering and indexing
Topic 2: Executing Queries using Databricks SQL and Databricks SQL Warehouses	20%	- Creating and managing views - Joining and combining datasets - Warehouse configuration and performance - ANSI SQL syntax and functions - Aggregations and grouping
Topic 3: Managing Data	8%	- Discovering and registering datasets - Dataset versioning and management - Data cleaning and preparation - Unity Catalog usage
Topic 4: Developing, Sharing, and Maintaining AI/BI Genie Spaces	12%	- Genie space setup and configuration - Natural language query setup - Access control and sharing - Maintenance and improvement
Topic 5: Importing Data	5%	- Delta Sharing - S3 and cloud storage integration - UI-based data ingestion - API and Auto Loader - Databricks Marketplace
Topic 6: Creating Dashboards and Visualizations in Databricks	16%	- Visualization types and best practices - Dashboard creation and layout - Filtering and interactivity - Scheduling and sharing dashboards
Topic 7: Data Modeling with Databricks SQL	5%	- Delta table structure - Performance-oriented modeling - Schema design principles
Topic 8: Understanding of Databricks Data Intelligence Platform	11%	- Lakehouse platform fundamentals - Core architecture and components - Workspace navigation and interface
Topic 9: Securing Data	8%	- Data governance policies - Access control and permissions - Secure storage and compliance

>> Latest Databricks-Certified-Data-Analyst-Associate Exam Topics <<

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
A data analyst needs to create an empty managed table table_name in database database_name with a specific schema. The table needs to be recreated and empty, regardless of whether or not the table already exists.
Which command can the analyst use to complete the task?

A. CREATE OR REPLACE TABLE database_name.table_name (width INT, length INT, height INT);
B. CREATE TABLE database_name.table_name USING (width INT, length INT, height INT);
C. CREATE OR REPLACE TABLE table_name FROM database_name USING (width INT, length INT, height INT);
D. CREATE OR REPLACE TABLE database_name.table_name USING (width INT, length INT, height INT);

Answer: A

Explanation:
The correct answer is C because the task requires creating or replacing a managed table with an explicitly defined schema. In Databricks SQL, column definitions are placed directly after the table name inside parentheses. The USING clause is for specifying a data source format, not for defining columns. Option C correctly uses CREATE OR REPLACE TABLE database_name.table_name (...), which recreates the table if it already exists and creates an empty table with the specified schema.
Official documentation extract used: Databricks CREATE TABLE syntax supports [CREATE OR] REPLACE followed by the table name and a table_specification containing column identifiers and column types.

NEW QUESTION # 19
Which of the following layers of the medallion architecture is most commonly used by data analysts?

A. All of these layers are used equally by data analysts
B. Bronze
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. References: What is the medallion lakehouse architecture?

NEW QUESTION # 20
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?

A. A categorical variable in which the number of categories continues to increase over time
B. A quantitative variable that never stops changing
C. A quantitative variable that can take on an uncountable set of values
D. A quantitative variable Chat can take on a finite or countably infinite set of values

Answer: C

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 # 21
A data analyst is processing a complex aggregation on a table with zero null values and the query returns the following result:
Which query did the analyst execute in order to get this result?

Answer: A

NEW QUESTION # 22
Which of the following should data analysts consider when working with personally identifiable information (PII) data?

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

Answer: E

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

NEW QUESTION # 23
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