Aug-2026 Pass Databricks Databricks-Certified-Data-Analyst-Associate Exam in First Attempt Easily [Q26-Q46]

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Aug-2026 Pass Databricks Databricks-Certified-Data-Analyst-Associate Exam in First Attempt Easily

Free Databricks-Certified-Data-Analyst-Associate Exam Files Downloaded Instantly 100% Dumps & Practice Exam

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

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

NEW QUESTION 26
Which statement describes descriptive statistics?

A branch of statistics that uses a variety of data analysis techniques to infer properties of an underlying distribution of probability.

A branch of statistics that uses summary statistics to categorically describe and summarize data.

A branch of statistics that uses summary statistics to quantitatively describe and summarize data.

A branch of statistics that uses quantitative variables that must take on a finite or countably infinite set of values.

NEW QUESTION 27
A data analyst is troubleshooting a query in Databricks SQL that fails when processing large datasets and complex join operations. Logs indicate that the job consistently aborts due to resource constraint errors on the cluster.
Which Query Profile metric should the analyst use to identify the operator that is causing resource overuse?

Time spent per operator

Shuffle read size per operator

Memory peak per operator

Bytes spilled to disk per operator

NEW QUESTION 28
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level data. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

The required compute resources could be costly

The gold-level tables are not appropriately clean for business reporting

The streaming data is not an appropriate data source for a dashboard

The streaming cluster is not fault tolerant

The dashboard cannot be refreshed that quickly

A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
B) The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
C) The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
D) The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
E) The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. Reference: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

NEW QUESTION 29
In which of the following situations should a data analyst use higher-order functions?

When custom logic needs to be applied to simple, unnested data

When custom logic needs to be converted to Python-native code

When custom logic needs to be applied at scale to array data objects

When built-in functions are taking too long to perform tasks

When built-in functions need to run through the Catalyst Optimizer

NEW QUESTION 30
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;

Option A

Option B

Option C

Option D

Option E

NEW QUESTION 31
A data analyst has two data sources that are providing similar but complementary information. The analyst wants to combine these sources of data into a single, comprehensive dataset for ongoing use for their team in a variety of different projects.
Which term is used to describe this type of work?

Last-mile ETL

Ad-hoc improvements

Data testing

Data blending

NEW QUESTION 32
Which of the following layers of the medallion architecture is most commonly used by data analysts?

None of these layers are used by data analysts

Gold

All of these layers are used equally by data analysts

Silver

Bronze

NEW QUESTION 33
Which of the following is a benefit of Databricks SQL using ANSI SQL as its standard SQL dialect?

It has increased customization capabilities

It is easy to migrate existing SQL queries to Databricks SQL

It allows for the use of Photon’s computation optimizations

It is more performant than other SQL dialects

It is more compatible with Spark’s interpreters

NEW QUESTION 34
A data analyst has been asked to use the below table sales_table to rank products within region by the sales.
Input table:
region product sales
WEST A 1880.59
EAST A 2045.99
EAST B 4583.23
WEST B 3391.19
The result of the query should look like this:
region product rank
EAST B 1
EAST A 2
WEST B 1
WEST A 2
Which query will accomplish this task?

SELECT region, product, RANK() OVER ( ORDER BY sales DESC ) AS rankFROM sales_table;

SELECT region, product, RANK() OVER ( PARTITION BY product ORDER BY sales DESC ) AS rankFROM sales_table;

SELECT region, product, RANK() OVER ( PARTITION BY region ) AS rankFROM sales_table;

SELECT region, product, RANK() OVER ( PARTITION BY region ORDER BY sales DESC ) AS rankFROM sales_table;

Option A

Option B

Option C

Option D

NEW QUESTION 35
A data engineering team has created a Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables. The microbatches are triggered every minute.
A data analyst has created a dashboard based on this gold-level dat
a. The project stakeholders want to see the results in the dashboard updated within one minute or less of new data becoming available within the gold-level tables.
Which of the following cautions should the data analyst share prior to setting up the dashboard to complete this task?

The required compute resources could be costly

The gold-level tables are not appropriately clean for business reporting

The streaming data is not an appropriate data source for a dashboard

The streaming cluster is not fault tolerant

The dashboard cannot be refreshed that quickly

A Structured Streaming pipeline that processes data in micro-batches and populates gold-level tables every minute requires a high level of compute resources to handle the frequent data ingestion, processing, and writing. This could result in a significant cost for the organization, especially if the data volume and velocity are large. Therefore, the data analyst should share this caution with the project stakeholders before setting up the dashboard and evaluate the trade-offs between the desired refresh rate and the available budget. The other options are not valid cautions because:
B . The gold-level tables are assumed to be appropriately clean for business reporting, as they are the final output of the data engineering pipeline. If the data quality is not satisfactory, the issue should be addressed at the source or silver level, not at the gold level.
C . The streaming data is an appropriate data source for a dashboard, as it can provide near real-time insights and analytics for the business users. Structured Streaming supports various sources and sinks for streaming data, including Delta Lake, which can enable both batch and streaming queries on the same data.
D . The streaming cluster is fault tolerant, as Structured Streaming provides end-to-end exactly-once fault-tolerance guarantees through checkpointing and write-ahead logs. If a query fails, it can be restarted from the last checkpoint and resume processing.
E . The dashboard can be refreshed within one minute or less of new data becoming available in the gold-level tables, as Structured Streaming can trigger micro-batches as fast as possible (every few seconds) and update the results incrementally. However, this may not be necessary or optimal for the business use case, as it could cause frequent changes in the dashboard and consume more resources. Reference: Streaming on Databricks, Monitoring Structured Streaming queries on Databricks, A look at the new Structured Streaming UI in Apache Spark 3.0, Run your first Structured Streaming workload

NEW QUESTION 36
A data analyst filters rows where the tags array includes the value ‘ sql ‘ using this query:
SELECT *
FROM main.analytics.articles
WHERE tags = ‘ sql ‘ ;
This query returns no results.
How should the analyst query to filter for rows where the tags array contains ‘ sql ‘ ?

SELECT * FROM main.analytics.articles WHERE tags IN ( ‘ sql ‘ );

SELECT * FROM main.analytics.articles WHERE tags LIKE ‘ %sql% ‘ ;

SELECT * FROM main.analytics.articles WHERE ‘ sql ‘ IN tags;

SELECT * FROM main.analytics.articles WHERE array_contains(tags, ‘ sql ‘ );

NEW QUESTION 37
What is an advantage of using a Delta Lake-based data lakehouse over classic enterprise data warehouse solutions?

Open-source formats

Schema enforcement

ACID transactions

Generic optimizations

NEW QUESTION 38
Which location can be used to determine the owner of a managed table?

Review the Owner field in the table page using Catalog Explorer

Review the Owner field in the database page using Data Explorer

Review the Owner field in the schema page using Data Explorer

Review the Owner field in the table page using the SQL Editor

NEW QUESTION 39
Where can an admin or data owner grant database, table, and view permissions to a group?

Dashboard

Data

SQL Warehouses

Settings

NEW QUESTION 40
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?

The query is using count(‘). which will count all the customers in the customers table, no matter the region.

The query is missing a GROUP BY region clause.

The query is using ORDER BY. which is not allowed in an aggregation.

There are no mistakes in the query.

The query is selecting region but region should only occur in the ORDER BY clause.

NEW QUESTION 41
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

Databricks Filesystem

Jobs

Dashboards

Repos

Data Explorer

NEW QUESTION 42
In which of the following situations should a data analyst use higher-order functions?

When custom logic needs to be applied to simple, unnested data

When custom logic needs to be converted to Python-native code

When custom logic needs to be applied at scale to array data objects

When built-in functions are taking too long to perform tasks

When built-in functions need to run through the Catalyst Optimizer

NEW QUESTION 43
Data engineers and data analysts are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

The pipeline can have different notebook sources in SQL and Python.

The pipeline will need to be written entirely in SQL.

The pipeline will need to use a batch source in place of a streaming source.

The pipeline will need to be written entirely in Python.

NEW QUESTION 44
A data analyst creates a Databricks SQL Query where the result set has the following schema:
region STRING
number_of_customer INT
When the analyst clicks on the ” Add visualization ” button on the SQL Editor page, which of the following types of visualizations will be selected by default?

Violin Chart

Line Chart

IBar Chart

Histogram

There is no default. The user must choose a visualization type.

NEW QUESTION 45
A data analyst is using Databricks Unity Catalog. The datasets are tagged by sensitivity, and confidential data is marked with the tag key confidential. The data analyst needs to quickly find all tables tagged as confidential to review their access permissions in the Databricks workspace search bar.
Which search key text should the data analyst use to find these tables?

confidential:true

tag=confidential

tag:confidential

search tag = ‘ confidential ‘

NEW QUESTION 46
A data organization has a team of engineers developing data pipelines following the medallion architecture using Delta Live Tables. While the data analysis team working on a project is using gold-layer tables from these pipelines, they need to perform some additional processing of these tables prior to performing their analysis.
Which of the following terms is used to describe this type of work?

Data blending

Last-mile

Data testing

Last-mile ETL

Data enhancement

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

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