Databricks Databricks-Certified-Data-Analyst-Associate Valid Test Prep & Databricks-Certified-Data-Analyst-Associate Valid Exam Question

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

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

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Databricks Databricks-Certified-Data-Analyst-Associate Exam Questions [2026]-Achieve Highest Scores

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q109-Q114):

NEW QUESTION # 109
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary. Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).dropField( " age " )
B. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).drop( " age " )
C. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 0.1).drop( " age " )
D. employees_df = employees_df.withColumn( " tax " , lit(0.1) * col( " salary " )).dropField( " age " )

Answer: C

Explanation:
Option B is correct after correcting the uploaded typo withcolumn to withColumn. A 10% tax is calculated by multiplying salary by 0.1, not by 10. The age column should be removed using .drop( " age " ). Options A and D multiply salary by 10, which calculates 1000%, not 10%. Options A and C also use dropField, which is not the correct DataFrame method for removing a top-level column. Official Databricks PySpark documentation states that withColumn adds or replaces a column and that drop(*cols) returns a new DataFrame without the specified columns.

NEW QUESTION # 110
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. Tracking usage of feature variables for machine learning projects
B. Segmenting customers into like groups using a clustering algorithm
C. Combining two data sources into a single, comprehensive dataset
D. Automating complex notebook-based workflows with multiple tasks
E. Testing the quality of data as it is imported from a source

Answer: C

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 # 111
How can a data analyst determine if query results were pulled from the cache?

A. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
B. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
C. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
D. Go to the Alerts tab and check the Cache Status alert.
E. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.

Answer: A

Explanation:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status.
Reference link: Databricks SQL - Query Cache

NEW QUESTION # 112
A data analyst has been asked to count the number of customers in each region and has written the following query:

If there is a mistake in the query, which of the following describes the mistake?

A. The query is using ORDER BY. which is not allowed in an aggregation.
B. There are no mistakes in the query.
C. The query is missing a GROUP BY region clause.
D. The query is selecting region but region should only occur in the ORDER BY clause.
E. The query is using count('). which will count all the customers in the customers table, no matter the region.

Answer: C

Explanation:
In the provided SQL query, the data analyst is trying to count the number of customers in each region. However, they made a mistake by not including the "GROUP BY" clause to group the results by region. Without this clause, the query will not return counts for each distinct region but rather an error or incorrect result. Reference: The need for a GROUP BY clause in such queries can be understood from Databricks SQL documentation: Databricks SQL.
I also noticed that you uploaded an image with your question. The image shows a snippet of an SQL query written in plain text on a white background. The query is attempting to select regions and count customers from a "customers" table and order the results by region. There's no visible syntax highlighting or any other color - it's monochromatic. The query is the same as the one in your question. I'm not sure why you included the image, but maybe you wanted to show me the exact format of your query. If so, you can also use code blocks to display formatted content such as SQL queries. For example, you can write:
SELECT region, count(*) AS number_of_customers
FROM customers
ORDER BY region;
This way, you can avoid uploading images and make your questions more clear and concise. I hope this helps.

NEW QUESTION # 113
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 quantitative variable Chat can take on a finite or countably infinite set of values
B. A quantitative variable that never stops changing
C. A quantitative variable that can take on an uncountable set of values
D. A categorical variable in which the number of categories continues to increase over time

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 # 114
......

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