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

Section	Weight	Objectives
Topic 1: Understanding of Databricks Data Intelligence Platform	11%	- Platform Components 1. Unity Catalog 2. Databricks SQL 3. Delta Lake 4. Lakeflow Jobs 5. Mosaic AI - Catalog and Governance 1. Views and Certified Tables 2. Managed and External Tables 3. Data Lineage 4. Catalogs and Schemas
Topic 2: Creating Dashboards and Visualizations	16%	- Visualization 1. Alerts and Filters 2. Dashboards 3. Charts and Graphs 4. Sharing and Publishing
Topic 3: Importing Data	5%	- Data Import 1. Creating Tables from Imported Data 2. Uploading Files 3. Loading CSV and JSON Data
Topic 4: Executing Queries Using Databricks SQL and SQL Warehouses	20%	- SQL Querying 1. Filtering and Aggregation 2. Writing SQL Queries 3. Query Optimization 4. SQL Warehouses 5. Joins and Subqueries
Topic 5: Analyzing Queries	15%	- Query Analysis 1. Monitoring Query Execution 2. Reading Query Plans 3. Performance Tuning
Topic 6: Managing Data	8%	- Data Management 1. Cleaning Data with SQL 2. Discovering Data Assets 3. Handling Missing and Invalid Data 4. Tagging and Lineage
Topic 7: Data Modeling with Databricks SQL	5%	- Modeling Concepts 1. Data Organization 2. Fact and Dimension Tables 3. Views and Relationships
Topic 8: Developing AI/BI Genie Spaces	12%	- AI and BI Features 1. Workspace Configuration 2. Natural Language Queries 3. AI/BI Genie
Topic 9: Securing Data	8%	- Security and Access Control 1. Data Access Policies 2. Unity Catalog Security 3. Permissions and Roles

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
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?

A. Decide on a single data visualization to add to the dashboard.
B. Add two separate visualizations to the dashboard based on the same Query.
C. Alter the Query to return two separate sets of results.
D. Copy the Query and create one data visualization per query.

Answer: B

NEW QUESTION # 75
Which data lakehouse feature results in improved data quality over a traditional data lake?

A. A data lakehouse provides storage solutions for structured and unstructured data.
B. A data lakehouse stores data in open formats.
C. A data lakehouse allows the use of SQL queries to examine data.
D. A data lakehouse supports ACID-compliant transactions.

Answer: D

Explanation:
Option D is correct. ACID-compliant transactions improve reliability and data quality because writes are consistent and atomic rather than leaving partially written or corrupted states. Open formats and SQL access are important lakehouse capabilities, but the feature most directly tied to improved data quality over a traditional data lake is ACID transactions. Official Databricks extract: Delta Lake extends Parquet with a transaction log for "ACID transactions," and Databricks states that Delta Lake adds ACID transactions and schema evolution for reliable, high-quality data.

NEW QUESTION # 76
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 data was collected
B. Organization-specific best practices for Pll data
C. None of these considerations
D. All of these considerations
E. Legal requirements for the area in which the analysis is being performed

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 # 77
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 USING (width INT, length INT, height INT);
B. CREATE OR REPLACE TABLE database_name.table_name (width INT, length INT, height INT);
C. CREATE TABLE database_name.table_name USING (width INT, length INT, height INT);
D. CREATE OR REPLACE TABLE table_name FROM database_name USING (width INT, length INT, height INT);

Answer: B

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 # 78
A data analyst has created a Query in Databricks SQL, and now they want to create two data visualizations from that Query and add both of those data visualizations to the same Databricks SQL Dashboard.
Which of the following steps will they need to take when creating and adding both data visualizations to the Databricks SQL Dashboard?

A. They will need to create two separate dashboards.
B. They will need to add two separate visualizations to the dashboard based on the same Query.
C. They will need to copy the Query and create one data visualization per query.
D. They will need to alter the Query to return two separate sets of results.
E. They will need to decide on a single data visualization to add to the dashboard.

Answer: B

Explanation:
A data analyst can create multiple visualizations from the same query in Databricks SQL by clicking the + button next to the Results tab and selecting Visualization. Each visualization can have a different type, name, and configuration. To add a visualization to a dashboard, the data analyst can click the vertical ellipsis button beneath the visualization, select + Add to Dashboard, and choose an existing or new dashboard. The data analyst can repeat this process for each visualization they want to add to the same dashboard. References: Visualization in Databricks SQL, Visualize queries and create a dashboard in Databricks SQL

NEW QUESTION # 79
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