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

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

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
Which of the following data workloads will utilize a Gold table as its source?

A. A job that ingests raw data from a streaming source into the Lakehouse.
B. A job that cleans data by removing malformatted records.
C. A job that aggregates uncleaned data to create standard summary statistics.
D. A job that enriches data by parsing its timestamps into a human-readable format.
E. A job that queries aggregated data designed to feed into a dashboard.

Answer: E

Explanation:
Option D is correct. Gold tables are refined, business-ready datasets used for analytics, dashboards, reporting, and other downstream consumption. A workload that queries aggregated data designed to feed a dashboard is therefore using Gold-layer data. Raw streaming ingestion belongs to Bronze, cleaning malformed records belongs to Silver, and aggregating uncleaned data is not the recommended Gold pattern. Official Databricks extract: the Gold layer "consists of aggregated data tailored for analytics and reporting" and is optimized for
"queries and dashboards."

NEW QUESTION # 26
Consider the following two statements:
Statement 1:

Statement 2:

Which of the following describes how the result sets will differ for each statement when they are run in Databricks SQL?

A. Both statements will fail because Databricks SQL does not support those join types.
B. When the first statement is run, all rows from the customers table will be returned and only the customer_id from the orders table will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
C. When the first statement is run, only rows from the customers table that have at least one match with the orders table on customer_id will be returned. When the second statement is run, only those rows in the customers table that do not have at least one match with the orders table on customer_id will be returned.
D. The first statement will return all data from the customers table and matching data from the orders table. The second statement will return all data from the orders table and matching data from the customers table. Any missing data will be filled in with NULL.
E. There is no difference between the result sets for both statements.

Answer: C

Explanation:
Based on the images you sent, the two statements are SQL queries for different types of joins between the customers and orders tables. A join is a way of combining the rows from two table references based on some criteria. The join type determines how the rows are matched and what kind of result set is returned. The first statement is a query for a LEFT SEMI JOIN, which returns only the rows from the left table reference (customers) that have a match with the right table reference (orders) on the join condition (customer_id). The second statement is a query for a LEFT ANTI JOIN, which returns only the rows from the left table reference (customers) that have no match with the right table reference (orders) on the join condition (customer_id). Therefore, the result sets for the two statements will differ in the following way:
The first statement will return a subset of the customers table that contains only the customers who have placed at least one order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT SEMI JOIN does not include any columns from the orders table.
The second statement will return a subset of the customers table that contains only the customers who have not placed any order. The number of rows returned will be less than or equal to the number of rows in the customers table, depending on how many customers have no orders. The number of columns returned will be the same as the number of columns in the customers table, as the LEFT ANTI JOIN does not include any columns from the orders table.
The other options are not correct because:
A) The first statement will not return all data from the customers table, as it will exclude the customers who have no orders. The second statement will not return all data from the orders table, as it will exclude the orders that have a matching customer. Neither statement will fill in any missing data with NULL, as they do not return any columns from the other table.
C) There is a difference between the result sets for both statements, as explained above. The LEFT SEMI JOIN and the LEFT ANTI JOIN are not equivalent operations and will produce different outputs.
D) Both statements will not fail, as Databricks SQL does support those join types. Databricks SQL supports various join types, including INNER, LEFT OUTER, RIGHT OUTER, FULL OUTER, LEFT SEMI, LEFT ANTI, and CROSS. You can also use NATURAL, USING, or LATERAL keywords to specify different join criteria.
E) The first statement will not return only the customer_id from the orders table, as it will return all columns from the customers table. The second statement is correct, but it is not the only difference between the result sets.

NEW QUESTION # 27
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?

A. Temporary view
B. Delta Table
C. Database
D. View
E. Spark SQL Table

Answer: A

Explanation:
Option D is correct. A temporary view is session-scoped and does not store physical data. It is suitable when the relational object is only needed in the current session or query context and should not be available to others in other sessions. A regular view also avoids copying physical data, but it is persistent and can be shared beyond the current session. Official Databricks extract: a view is a "virtual table that has no physical data," and temporary views are scoped to the notebook/script or query level and cannot be referenced outside that scope.

NEW QUESTION # 28
Which statement describes descriptive statistics?

A. A branch of statistics that uses summary statistics to quantitatively describe and summarize data.
B. A branch of statistics that uses summary statistics to categorically describe and summarize data.
C. A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.
D. A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

Answer: A

NEW QUESTION # 29
Data professionals with varying responsibilities use the Databricks Lakehouse Platform Which role in the Databricks Lakehouse Platform use Databricks SQL as their primary service?

A. Business analyst
B. Data engineer
C. Data scientist
D. Platform architect

Answer: A

Explanation:
In the Databricks Lakehouse Platform, business analysts primarily utilize Databricks SQL as their main service. Databricks SQL provides an environment tailored for executing SQL queries, creating visualizations, and developing dashboards, which aligns with the typical responsibilities of business analysts who focus on interpreting data to inform business decisions. While data scientists and data engineers also interact with the Databricks platform, their primary tools and services differ; data scientists often engage with machine learning frameworks and notebooks, whereas data engineers focus on data pipelines and ETL processes. Platform architects are involved in designing and overseeing the infrastructure and architecture of the platform. Therefore, among the roles listed, business analysts are the primary users of Databricks SQL.

NEW QUESTION # 30
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