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.