Free DATABRICKS-CERTIFIED-DATA-ANALYST-ASSOCIATE Practice Test Questions and Answers (2026)

The stakeholder's requirement is for the dashboard to refresh at least every minute to reflect new data in the gold-level tables. In Databricks, refreshing a dashboard executes the underlying SQL queries on a SQL warehouse. Setting a refresh schedule to every minute means the SQL warehouse will be actively running queries continuously. This constant compute usage consumes Databricks Units (DBUs), which can lead to significant and potentially high operational costs. Therefore, it is a critical responsibility for the data analyst to caution stakeholders about the cost implications of such a high-frequency refresh requirement.

B. The gold-level tables are not appropriately clean for business reporting: This is incorrect. In the Medallion architecture, gold-level tables are specifically curated, aggregated, and cleaned for business intelligence and reporting purposes.

C. The streaming data is not an appropriate data source for a dashboard: This is false. A key use case for Databricks is to enable analytics and dashboarding on near real-time data that has been processed by a streaming pipeline and landed in a table.

D. The streaming cluster is not fault tolerant: This is incorrect. Databricks Structured Streaming is designed with fault tolerance as a core feature, ensuring data integrity and consistent processing even if failures occur.

E. The dashboard cannot be refreshed that quickly: This is factually incorrect. Databricks dashboards can be scheduled to refresh as frequently as every one minute, making the stakeholder's request technically feasible.

1. Dashboard Refresh Scheduling and Cost: Databricks Documentation

"Schedule a dashboard for periodic refreshes". This document shows that refresh intervals as low as "Every 1 minute" are available. It also notes

"When a dashboard is refreshed on a schedule

Databricks runs all queries in the dashboard. The user who saved the schedule is the one whose credentials are used to run the queries." This implies compute usage.

Source: Databricks Documentation > Databricks SQL > Dashboards > Schedule a dashboard for periodic refreshes.

2. SQL Warehouse Compute and Cost: Databricks Documentation

"What is a Databricks SQL warehouse?". This document explains that SQL warehouses are the compute resources for running queries. "The cost of a SQL warehouse is determined by its cluster size... and the number of clusters." Frequent queries directly translate to sustained compute usage and cost.

Source: Databricks Documentation > Databricks SQL > Administration > What is a Databricks SQL warehouse?

3. Gold-Level Tables: Databricks Documentation

"The medallion architecture". This guide defines the Gold layer as tables for "reporting and business intelligence" that are "often aggregated and contain broad

denormalized data."

Source: Databricks Documentation > Data management > Best practices > The medallion architecture.

4. Structured Streaming Fault Tolerance: Databricks Documentation

"Structured Streaming". The documentation states

"Structured Streaming provides fault tolerance and exactly-once data consistency for streaming queries."

Source: Databricks Documentation > Apache Spark > Structured Streaming.

The correct syntax to invoke a user-defined function (UDF) in a SQL query is to use the function's name followed by parentheses containing the arguments. In this case, the function price requires two arguments, which are provided by the customerspend and customerunits columns. The AS clause is then used to alias the output of the function to the new column name customerprice. This syntax directly applies the defined function row by row to the specified columns in the customersummary table.

A: This syntax is invalid. PRICE is not a SQL operator; functions must be called with parentheses () enclosing their arguments.

B: This attempts to select a column named price, not call the function price with the required column arguments.

C: The function() wrapper is not a valid SQL construct. The UDF is called directly by its name without this extra keyword.

D: This is redundant. The UDF price is already defined to return a DOUBLE, so casting the result again is unnecessary.

1. Databricks SQL Language Reference

CREATE FUNCTION (SQL): The documentation provides examples of creating and then invoking a SQL UDF. The invocation syntax is shown as functionname(arguments). For instance

after creating a function

it is called within a SELECT statement like SELECT myudf(c1) FROM .... This supports the structure price(customerspend

customerunits).

Source: Databricks Documentation > SQL reference > Data definition language > CREATE FUNCTION (SQL).

2. Databricks SQL Language Reference

SELECT: The documentation for the SELECT statement specifies that the selectclause can contain expressions

which include function calls. The standard syntax for a function call is functionname([ ALL | DISTINCT ] expression [

...]). This confirms that price(customerspend

customerunits) is the correct expression.

Source: Databricks Documentation > SQL reference > Data manipulation language > SELECT.

When a query result contains a string column (categorical data) and a numeric column (a measure), Databricks SQL's auto-charting feature defaults to a bar chart. This visualization type is optimal for comparing a numerical value across distinct categories. In this scenario, the region column serves as the discrete dimension for the X-axis, and the numberofcustomer column provides the corresponding numerical value for the Y-axis, making a bar chart the most logical and automatically selected representation.

A. Violin Chart: This chart is used to visualize the distribution of numerical data, not for comparing a single aggregate value per category. It is not a typical default.

B. Line Chart: Line charts are the default for data with a temporal or continuous numerical component (like a date or timestamp) to show trends, which is not present here.

D. Histogram: A histogram is used to plot the frequency distribution of a single numerical column. The query result has two columns (one categorical, one numerical).

E. There is no default: This is incorrect. Databricks SQL automatically selects a best-effort visualization type based on the query result's schema to provide an initial view.

1. Databricks Documentation

"Visualization types in Databricks": This document describes the use cases for different chart types. It states

"Bar charts represent the change in metrics over a discrete dimension or show proportionality." This directly matches the provided schema of region (discrete dimension) and numberofcustomer (metric).

2. Databricks Documentation

"Create a new visualization": This guide confirms the auto-selection behavior: "When you click Add visualization

Databricks SQL automatically creates a visualization that best matches your query result. It makes a best-effort choice of a useful visualization." This refutes the "no default" option and supports the logic of choosing the most appropriate chart

which is a bar chart in this case.

When a query parameter is mapped to a "Dashboard Parameter," it creates a control at the dashboard level. Any change to this dashboard parameter's value will be passed to the underlying query, causing the associated visualization (the area chart) to refresh with new data. The primary benefit of this feature is that the same dashboard parameter can be linked to multiple visualizations on the dashboard. Therefore, changing the value in the single dashboard parameter control will simultaneously update the area chart and all other visualizations that are also linked to that same parameter, providing a unified and interactive dashboard experience.

A: This is incorrect because other visualizations on the dashboard can also be linked to the same dashboard parameter and will update accordingly.

C: This is incorrect. The value is not static; the purpose of a dashboard parameter is to allow users to dynamically change the value after the chart has been added.

D: This is incorrect. A dashboard parameter is explicitly designed to be interactive and changeable by the end-user viewing the dashboard.

E: This is a nonsensical statement. A visualization (the area chart) is an object that displays data; it does not convert into a parameter, which is a control element.

1. Databricks Documentation

"Dashboard parameters": This document explains the functionality of dashboard parameters. It states

"You can link parameters to create a filter for your dashboard. For example

you can create a dashboard-level filter that lets users select from a list of values. When a value is selected

the visualizations on the dashboard are updated to reflect the selected value." This supports the idea that a single dashboard parameter updates all linked visualizations.

2. Databricks Documentation

"Use parameters in dashboards": This guide details the process. It shows how to "Add a widget that uses a query parameter" and then configure it to use a "New dashboard parameter" or an "Existing dashboard parameter." The ability to link to an "Existing dashboard parameter" confirms that multiple widgets (visualizations) can be controlled by the same parameter. The documentation states

"When you link parameters in this way

a change in the source parameter selection widget causes the linked queries to rerun using the new value." This directly supports option B.

The primary benefit of Databricks SQL adopting the ANSI SQL standard is to ensure high compatibility with other SQL-based systems. This standardization significantly simplifies the migration process for organizations moving from traditional data warehouses or other platforms. Analysts and developers can reuse their existing SQL queries, scripts, and business intelligence tool configurations with minimal to no modification. This reduces the effort, cost, and time required for adoption, as it leverages the widespread familiarity and portability of the ANSI SQL syntax.

A: Adhering to a standard like ANSI SQL prioritizes compatibility and predictability over vendor-specific customizations.

C: Photon is Databricks' vectorized execution engine. Its performance benefits are a feature of the platform's architecture, not a direct result of using the ANSI SQL dialect.

D: A SQL dialect is a syntax and semantic standard; it is not inherently more or less performant than another. Performance is determined by the underlying query engine.

E: While Databricks SQL is built on Spark SQL, the key benefit of ANSI compliance is for easier migration from external systems, not just for internal compatibility.

1. Databricks Documentation

"What is Databricks SQL?": "Databricks SQL supports the ANSI SQL standard

so you can use the syntax you're already familiar with to work with your data." This highlights the ease of use for those with existing SQL knowledge

which is foundational to easy migration.

2. Databricks Documentation

"ANSI compliance in Databricks Runtime": "Spark SQL has two options for SQL dialect compliance: spark.sql.ansi.enabled=false (default) and spark.sql.ansi.enabled=true... For easier migration from traditional data warehouses

we recommend setting spark.sql.ansi.enabled to true." This directly links ANSI compliance with the benefit of easier migration.

3. Databricks Documentation

"Migrate and interoperate with Databricks SQL": "Because Databricks SQL supports ANSI standard SQL

you can migrate many of your existing queries from other data warehousing systems." This statement explicitly confirms that ANSI SQL support is a key enabler for migration.

Descriptive statistics is a branch of statistics focused on summarizing and organizing data to describe its main features. This is achieved by using quantitative measures such as mean, median, mode, standard deviation, and variance, as well as graphical representations like histograms and box plots. The primary goal is to provide a quantitative summary of the sample data, rather than making inferences about the larger population from which the data was drawn.

A: This describes inferential statistics, which uses sample data to make generalizations or predictions about a larger population.

B: The term "categorically describe" is too restrictive. While descriptive statistics can be applied to categorical data (e.g., frequency counts), its core involves quantitative summarization.

D: This describes discrete variables, which are a type of data that can be analyzed using descriptive statistics, but it is not the definition of the statistical branch itself.

1. Databricks Documentation

summary function: The summary function in Databricks computes descriptive statistics for numeric and string columns. For numeric columns

it calculates count

mean

stddev

min

and max

which are all quantitative summaries. This aligns with the definition of using summary statistics to quantitatively describe data.

Source: Databricks Documentation > Spark SQL > DataFrame API > pyspark.sql.DataFrame.summary.

2. University Courseware

Penn State University STAT 200: "Descriptive statistics consists of methods for organizing and summarizing information... Numerical methods for summarizing data include calculating measures of center

such as the mean or median

and measures of spread

such as the standard deviation or interquartile range." This directly supports the concept of using quantitative measures to summarize data.

Source: Penn State University

Eberly College of Science

STAT 200: Elementary Statistics

Lesson 1.1: "What is Statistics?".

3. University Courseware

MIT OpenCourseWare: "Descriptive statistics: describing data with graphs and numerical summaries." This definition emphasizes the use of numerical (quantitative) methods to describe the data.

Source: MIT OpenCourseWare

18.05 Introduction to Probability and Statistics

Spring 2014

Topic 1: "Introduction to Statistics".

The core requirement is to update the dashboard's visuals within 10 minutes of new data arriving in the gold-level tables. The mechanism designed for this purpose in Databricks is the dashboard refresh schedule. By setting a refresh schedule with an interval of 10 minutes or less, the queries that power the dashboard will automatically re-run at the specified frequency. This ensures that any new data populated by the streaming pipeline into the gold tables is promptly reflected in the dashboard, meeting the stakeholders' timeliness requirement.

B: An always-on SQL Warehouse reduces query start-up latency but does not trigger the refresh itself. The refresh timing is controlled by the schedule interval, not the warehouse's availability.

C: Including stakeholders as subscribers sends them a static snapshot via email. It is a distribution feature and does not control the data freshness of the live dashboard itself.

D: A Structured Streaming cluster is used for the data ingestion pipeline that populates the tables. Dashboard queries are executed on a SQL Warehouse, not a streaming cluster.

1. Databricks Documentation

"Schedule a dashboard for automatic refreshes": This document explicitly states

"You can schedule a dashboard to automatically refresh at a specified interval. When a dashboard is scheduled

Databricks runs the queries that back the dashboard visualizations and updates the visualizations with the latest data." This directly supports setting a scheduled interval to meet the requirement.

Source: Databricks Official Documentation > SQL > Dashboards > Schedule and share dashboards.

2. Databricks Documentation

"What are SQL warehouses?": This documentation describes SQL warehouses as the compute resources for running queries in Databricks SQL

including those for dashboards. It distinguishes the warehouse's role (compute execution) from the scheduling mechanism. The configuration of a warehouse (like auto-stop

which relates to the "always-on" concept) affects performance

not the trigger for a refresh.

Source: Databricks Official Documentation > SQL > Administration > What are SQL warehouses?.

3. Databricks Documentation

"Structured Streaming": This section details that Structured Streaming is a stream processing engine for building data pipelines. This confirms it is the technology used to populate the gold tables

not the technology used to query them for a dashboard.

Source: Databricks Official Documentation > Development > Structured Streaming.

Databricks SQL is designed to provide a high-performance, integrated environment for data analysts to run ad-hoc queries and create visualizations and dashboards directly within the Databricks platform. This capability is ideal for quick exploratory analysis, data validation, and rapid prototyping. It is intended to be a complementary tool that works alongside and powers other specialized business intelligence (BI) tools like Tableau and Power BI. These external tools connect to Databricks SQL as a data source to build more polished, production-grade, and interactive dashboards for wider business consumption.

A: Databricks SQL is not an exact substitute, as dedicated BI tools typically offer more extensive and mature visualization, customization, and presentation-layer features.

B: While it may have different functionality, its primary role is not to be a lesser substitute but to complement and power other tools.

C: It is not a complete replacement. It complements the BI ecosystem by providing a powerful query engine on the lakehouse, not by replacing the entire feature set of dedicated BI tools.

D: "Professional-grade presentations" with extensive branding and customization are the primary strength of tools like Tableau or Power BI, not the core use case for Databricks SQL's native dashboards.

1. Databricks Documentation

"What is Databricks SQL?": This document states

"Databricks SQL provides a general purpose SQL query editor

visualizations

and dashboards for ad-hoc exploratory queries and prototyping." It also clarifies its complementary role: "Databricks SQL also is the primary way to run SQL queries with other BI and visualization tools." This highlights its dual purpose as both an in-platform tool and a backend for external BI tools.

2. Databricks Documentation

"Connect BI tools to Databricks": The existence of extensive documentation and dedicated Partner Connect integrations for tools like Power BI

Tableau

and Looker demonstrates that Databricks SQL is designed to work with these tools

not replace them. It serves as the high-performance SQL data warehouse layer they query against.

The core problem is the long start-up time for a SQL endpoint that runs an intermittent query (every four hours). Serverless SQL endpoints (now called warehouses) are specifically designed to address this issue. They are managed by Databricks and provide "instant compute," eliminating the start-up latency associated with Classic or Pro endpoints, which must provision clusters in the customer's cloud account. This directly solves the start-up delay. The pay-per-use model of Serverless compute is also cost-effective for intermittent workloads, aligning with the goal of managing costs.

A. Reduce the SQL endpoint cluster size: This would likely slow down query execution and would not reduce the time it takes for the cluster to provision and start.

B. Increase the SQL endpoint cluster size: This might speed up query execution but would increase costs and does not address the initial start-up latency of the endpoint.

C. Turn off the Auto stop feature: This would eliminate start-up time for subsequent runs but would incur significant costs by keeping the cluster idle for nearly four hours between runs.

D. Increase the minimum scaling value: This setting is for handling query concurrency by adding more clusters, not for reducing the start-up time of the initial cluster.

1. Databricks Documentation

"What are SQL warehouses?": This document explicitly contrasts the different types of SQL warehouses. It states

"Serverless SQL warehouses... provide instant compute... Pro and Classic SQL warehouses take several minutes to start." This directly supports the choice of Serverless for eliminating start-up time.

Source: Databricks Documentation > Data Warehousing > What are SQL warehouses? > SQL warehouse types.

2. Databricks Documentation

"Configure SQL warehouses": This page details the configuration options. Regarding "Auto stop

" it recommends keeping it enabled (e.g.

at 10 minutes) to "avoid paying for idle time

" which explains why option C is incorrect from a cost-management perspective. The "Scaling" section clarifies that this feature manages the number of clusters to handle query load

not initial start-up

making option D incorrect.

Source: Databricks Documentation > Data Warehousing > Configure SQL warehouses > General & Advanced options.

3. Databricks Documentation

"Serverless compute for SQL warehouses": This page reinforces the benefit of Serverless endpoints. It highlights

"Benefits of serverless SQL warehouses include: ... Fast start-up time for SQL warehouses." This confirms that Serverless is the intended solution for the problem described.

Source: Databricks Documentation > Data Warehousing > Serverless compute for SQL warehouses.

In Databricks SQL, a single query can serve as the data source for multiple, distinct visualizations. The standard workflow is to execute the query once, then create the first visualization from the results. Subsequently, you can create a second, different visualization from the same query results. Both of these independently created visualizations can then be added to a single dashboard. This approach is efficient as it avoids redundant query execution and maintenance, directly accomplishing the user's goal of having two visualizations from one query on one dashboard.

A. They will need to alter the Query to return two separate sets of results.

This is incorrect. A single result set from one query is sufficient to create multiple different visualizations.

C. They will need to create two separate dashboards.

This is incorrect. The objective is to add both visualizations to the same dashboard, which is a standard feature.

D. They will need to decide on a single data visualization to add to the dashboard.

This is incorrect as it contradicts the requirement to add two visualizations.

E. They will need to copy the Query and create one data visualization per query.

This is an inefficient and unnecessary step. Databricks SQL is designed to support creating multiple visualizations from a single query object.

1. Databricks Documentation

"Create and manage dashboards in Databricks SQL": This document outlines the process of adding visualizations to dashboards. It states

"You can add visualizations and text boxes to dashboards." The process described involves adding existing visualizations from queries

implying a many-to-one relationship (many visualizations to one dashboard).

Reference: Databricks SQL Guide > Dashboards > Create and manage dashboards in Databricks SQL.

2. Databricks Documentation

"Work with query results": This page explicitly confirms that a single query can have multiple visualizations.

Quote: "You can work with query results in the SQL editor to create visualizations and dashboards. ... You can create multiple visualizations on different tabs to help you interpret your query results."

Reference: Databricks SQL Guide > Queries > Work with query results.

3. Databricks Documentation

"Visualizations in Databricks": This document details how to create visualizations from query results. The interface allows adding a new visualization via a + button on the results tab

which can be done multiple times for the same query result set.

Reference: Databricks SQL Guide > Visualizations > Visualization types in Databricks.

A continuous variable is a type of quantitative variable that can assume an infinite, uncountable number of values within a given range. The defining characteristic is that for any two values, it is always possible to find another value between them. For example, height, weight, and temperature are continuous because a measurement can be refined to greater and greater precision (e.g., 70.1 inches, 70.11 inches, 70.112 inches). Histograms are the appropriate visualization to understand the distribution of such variables by grouping the uncountable values into a finite number of bins.

A: "Never stops changing" is an informal and imprecise phrase that is not a standard statistical definition for a continuous variable.

B: This is the definition of a discrete variable, which has a finite or countably infinite set of distinct values (e.g., number of employees).

D: This describes a high-cardinality categorical variable. Continuous variables are quantitative (numeric), not categorical (labels or groups).

1. Databricks Documentation

Data visualization in Databricks: While not providing a formal definition

the documentation states that a histogram "plots the frequency distribution of a numeric column." This directly supports the use case in the question

where histograms are created for continuous (numeric) variables. (See: Databricks Documentation > Visualizations > Histogram).

2. PennState

STAT 200: Elementary Statistics Courseware: In Lesson 1.2

"Types of Variables

" a continuous variable is defined as "a quantitative variable that has an infinite number of possible values that are not countable." This directly supports option C.

3. MIT OpenCourseWare

18.05 Introduction to Probability and Statistics

Spring 2014: The class notes for "Topic 2: Discrete and Continuous Variables" define a continuous random variable as one whose set of possible values is an entire interval of numbers

which is an uncountable set. This aligns perfectly with the correct answer. (See: MIT OCW

18.05

Topic 2 Notes

Section 1).

The core issue is that Databricks SQL alerts do not support dynamic evaluation using query parameters. When an alert is configured for a query that contains parameters, the alert's scheduled executions will only use the default values defined for those parameters in the query editor. The user's intent to have a configurable date range for the alert is not supported, as the alert will not prompt for new values or use different values on each run. Therefore, from the user's perspective of wanting a dynamic alert, the feature "does not work" as expected, making the use of parameters incompatible with this goal.

A. Alerts don't work with queries that access tables. This is incorrect. The primary function of alerts is to monitor data within tables.

B. Queries that return results based on dates cannot be used with Alerts. This is incorrect. Monitoring data based on dates (e.g., "today's sales") is a very common use case for alerts.

C. The wrong query parameter is being used. Alerts only work with Date and Time query parameters. This is incorrect. The limitation applies to all parameter types, not just Date.

E. The wrong query parameter is being used. Alerts only work with dropdown list query parameters, not dates. This is incorrect. The limitation is general to all query parameters, not specific to dates or dropdowns.

1. Databricks Documentation

"What are Databricks SQL alerts?": This document explicitly states the behavior of alerts with parameterized queries.

Reference: Under the section "Create an alert"

the documentation notes: "If the target query has parameters

the alert is evaluated using the default parameter values. To monitor the alert for specific parameter values

you must set them as the default in the target query." This confirms that parameters are not dynamic in the context of alerts.

Source: Databricks Official Documentation > Databricks SQL > What are Databricks SQL alerts?

In Databricks SQL, ad-hoc or saved SQL statements can be authored and executed only in the SQL Editor itself or when you open an existing (or create a new) item on the Queries page, which launches the same editor interface. Other pages (Data, Dashboards, Alerts) are limited to browsing objects, viewing visualizations, or managing alert rules and do not expose the code-editing/run environment needed to execute SQL.

A. Data page – browse schemas/tables; no query-execution interface.

B. Dashboards page – view dashboards; cannot edit or run SQL.

D. Alerts page – manage alert definitions; no place to write queries.

1. Databricks Documentation – “Run a query” (Databricks SQL): “You can run queries from the SQL Editor or from the Queries page.” (docs.databricks.com/sql/user/queries/run-query.html

section: Run a query).

2. Databricks Documentation – “SQL Editor” page: “Use the SQL Editor to write and execute SQL statements…” (docs.databricks.com/sql/user/sql-editor.html

first paragraph).

Databricks SQL allows dashboards to be automatically refreshed on a schedule. As part of this scheduling configuration, a data analyst can add a list of subscribers by their email addresses. When the dashboard successfully refreshes, an email with a snapshot of the dashboard is sent to all subscribers. This feature is specifically designed to share dashboard updates with stakeholders, including those who are not users in the Databricks workspace, thus accomplishing the task with minimal effort.

A: Granting permissions directly to the dashboard requires the recipients to be users within the Databricks workspace, which the question explicitly states is not permitted for these stakeholders.

C: Subscribing to a SQL Warehouse is not a feature for sharing dashboards. SQL Warehouses are the compute resources that run queries, not the shareable assets themselves.

D: This is a manual, high-effort process. The analyst would have to remember to download and email the PDF after every refresh, which contradicts the "minimal effort" requirement.

1. Databricks Documentation - Schedule and email dashboard snapshots:

Reference: In the "Schedule dashboard updates" section

the documentation states

"To schedule the dashboard to be automatically refreshed at a regular interval

click Schedule... You can optionally subscribe other users to receive email snapshots of the dashboard when it is refreshed."

Reference: In the "Subscribe to email snapshots" section

it clarifies

"You can subscribe other users

even if they are not users in your Databricks workspace... Click Add subscriber and enter the email address." This directly supports sharing with non-users via email subscription.

Source: Databricks Data Intelligence Platform Documentation > Dashboards > Schedule and email dashboard snapshots.

2. Databricks Documentation - Share dashboards:

Reference: This page describes using the Share button to manage permissions for users and groups within the workspace. It states

"If a user does not have at least CAN VIEW permission on a dashboard

they cannot see it." This confirms that direct sharing (Option A) requires a workspace account and permissions.

Source: Databricks Data Intelligence Platform Documentation > Dashboards > Share dashboards.

The most direct and efficient method for a data analyst to quickly upload a small CSV file and create a table in Databricks SQL is by using the built-in "Add data" UI. This feature is specifically designed for this use case, allowing users to upload files from their local machine directly through the web interface. The UI guides the user through the process, automatically inferring the schema and creating a managed Delta table, which is immediately available for querying in Databricks SQL. This single, streamlined workflow is significantly faster than multi-step alternatives.

B: Partner Connect is used to establish integrations with third-party data tools and services (like Fivetran or Power BI), not for direct file uploads from a user's desktop.

C: While functional, this method is less efficient. It requires the analyst to first access and upload the file to an external cloud storage location before creating the table in Databricks.

D: Manually copying and pasting data is highly impractical, error-prone, and not a standard or recommended procedure for data ingestion, even for a small number of rows.

1. Databricks Documentation

"Load data using the add data UI": This document explicitly describes the feature mentioned in option A. It states

"You can use the add data UI to load data from your local machine... Databricks stores these files as managed tables." This confirms it is the intended method for this scenario.

2. Databricks Documentation

"Ingest data into a Databricks lakehouse": This guide provides an overview of data ingestion methods. It positions the "Add data UI" as the simplest way to get started by loading local files

contrasting it with more advanced

scalable methods like Auto Loader which would be used for files already in cloud storage (as described in option C).

3. Databricks Documentation

"What is Databricks Partner Connect?": This source clarifies the purpose of Partner Connect

stating it "lets you create connections to our software and data partners from the Databricks UI." This confirms it is not a tool for direct file ingestion from a local machine.