Microsoft PL-900 Microsoft Power Platform Fundamentals Exam Dumps and Practice Test Questions Set 1 Q1-20

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Question 1: 

Which component of Microsoft Power Platform is primarily used to build low-code, responsive business applications that can run on web and mobile devices?

A) Power BI

B) Power Apps

C) Power Automate

D) Power Virtual Agents

Answer: B) Power Apps

Explanation:

A) Power BI is a data visualization and business intelligence tool used to create interactive reports and dashboards. It connects to many data sources and helps users analyze data through visualizations, charts, and reports. Power BI is focused on analytics and insights rather than application creation or workflow automation. While Power BI dashboards can be embedded in apps and shared across organizations, it is not the primary tool for building user-facing apps.

B) Power Apps is a low-code application platform designed for building custom business apps that run on web browsers and mobile devices. It provides a canvas app experience for drag-and-drop UI design and a model-driven approach for apps built on Dataverse. Power Apps connects to many data sources via connectors and allows business users and developers to create forms, screens, and logic with minimal coding. This is the component intended specifically to build responsive applications for business scenarios.

C) Power Automate (formerly Microsoft Flow) is a workflow and process automation service. It automates tasks by connecting different services and triggering actions based on events, schedules, or user inputs. Power Automate can be used inside apps to automate backend flows (for example, to create records, send notifications, or move data), but it does not itself provide the canvas or UI framework for building full-featured web and mobile apps.

D) Power Virtual Agents is a service for building chatbots using a no-code graphical interface. These chatbots can answer questions, collect information, and integrate with backend systems via Power Automate flows or connectors. While Virtual Agents can be embedded into pages or apps to provide conversational experiences, they are specifically for bot creation and not general-purpose app building.

Reasoning: The question asks which component is primarily used to build low-code responsive business applications for web and mobile. Power Apps is purpose-built for that task: it provides UI design, integrates with Dataverse and connectors, supports mobile responsiveness, and offers both canvas and model-driven approaches. While Power BI, Power Automate, and Power Virtual Agents play important roles in analytics, automation, and conversational interfaces respectively, they are complementary rather than the primary app-building platform. Therefore, Power Apps is the correct answer.

Question 2: 

What is Microsoft Dataverse used for in the Power Platform?

A) Creating visual reports and dashboards

B) Hosting relational data and business logic for apps

C) Automating repetitive tasks across services

D) Designing conversational chatbots

Answer: B) Hosting relational data and business logic for apps

Explanation:

A) Power BI creates visual reports and dashboards; this is for analytics and not the same as Dataverse.

B) Dataverse is a managed data platform that stores relational data, supports standard and custom tables, enforces business rules and security, and can host business logic (like business rules, plugins, and workflows). It provides a common schema and is deeply integrated with Power Apps and Power Automate, making it ideal for centralizing app data.

C) Automating tasks across services is the role of Power Automate, which uses connectors and flows for orchestration.

D) Designing chatbots is the role of Power Virtual Agents; it uses logic and integrations but not Dataverse as its primary purpose.

Reasoning: Dataverse is not for reporting (Power BI), automation (Power Automate), or bot design (Virtual Agents) per se. Its key value is as a secure, scalable data store closely integrated with the platform, supporting relational modeling, business rules, and plugin-based logic—therefore B is correct.

Question 3: 

Which Power Platform capability allows non-developers to automate a multi-step business process using a graphical designer and prebuilt connectors?

A) Power Apps portals

B) Power Automate

C) Dataverse tables

D) Power BI dataflows

Answer: B) Power Automate

Explanation: 

A) Power Apps portals enable external-facing websites for interacting with Dataverse data, not orchestration of multi-step automation in the way described.

B) Power Automate provides a graphical flow designer that lets users create automated workflows triggered by events, schedules, or manual actions. It includes hundreds of prebuilt connectors (e.g., SharePoint, Office 365, SQL Server, Dynamics, third-party services) and supports branching, approvals, loops, and error handling—all targeted at automating multi-step processes without traditional coding.

C) Dataverse tables are for storing data and hosting logic, not the orchestration of workflows across services.

D) Power BI dataflows are about data transformation and ETL within Power BI, not general business process automation across services.

Reasoning: The question highlights a non-developer friendly graphical designer plus prebuilt connectors to automate multi-step processes—this maps precisely to Power Automate. Thus B is correct.

Question 4: 

Which type of Power Apps is best suited for building highly customized pixel-perfect screens using drag-and-drop controls?

A) Model-driven app

B) Canvas app

C) Portal app

D) Dataverse app

Answer: B) Canvas app

Explanation: 

A) Model-driven app emphasizes data-driven UI generated from Dataverse metadata, suited for complex relational data but less for pixel-perfect custom layout.

B) Canvas app provides a blank canvas where makers place controls, images, and layout items with precise positioning and custom styling. It is ideal for creating highly tailored, pixel-perfect screens by dragging and dropping elements and writing formulas for behavior.

C) Portal app (Power Apps portals) provides external-facing websites for users outside the organization and uses templates and web components; it’s not the primary environment for pixel-perfect internal app screens.

D) Dataverse app is not a distinct app type—Dataverse is the data platform that model-driven and canvas apps can use. The term doesn’t denote the UI style requested.

Reasoning: The focus on pixel-perfect, drag-and-drop screen customization points directly to canvas apps. Model-driven apps auto-generate UI from data and metadata, so B is the correct answer.

Question 5: 

Which Power Platform feature helps secure data by defining who can see or edit specific records at row-level?

A) Environment roles

B) Row-level security (Dataverse security roles and field-level security)

C) Power BI workspace access

D) Connector permissions

Answer: B) Row-level security (Dataverse security roles and field-level security)

Explanation: 

A) Environment roles control access to environments and administrative privileges but are broader than record-level access.

B) Dataverse uses security roles, teams, and business units to enforce row-level security: security roles grant privileges (create, read, write, delete) scoped by owner or organization, field-level security controls access to specific fields, and record ownership plus sharing provides fine-grained row-level control.

C) Power BI workspace access controls who can view or edit Power BI content but doesn’t directly enforce row-level security across Dataverse tables (though Power BI supports row-level security on datasets).

D) Connector permissions manage access to external services for flows and apps but do not inherently provide row-level record security inside Dataverse.

Reasoning: The ability to restrict visibility and edit rights at the individual record (row) level is provided by Dataverse security constructs—security roles, teams, ownership, sharing, and field-level security. Therefore B is correct.

Question 6: 

Which Power Platform component would you use to build a chatbot that can answer FAQs and hand off to a human when needed?

A) Power Automate

B) Power Virtual Agents

C) Power BI

D) Power Apps

Answer: B) Power Virtual Agents

Explanation: 

A) Power Automate automates workflows and could be used to support handoffs but is not a chatbot creation tool.

B) Power Virtual Agents is a no-code chatbot builder that enables creation of topic-driven conversational bots, supports branching, integrates with Power Automate for executing flows, and includes handoff patterns to escalate to human agents via channels or notifications.

C) Power BI is for analytics and cannot build chatbots.

D) Power Apps builds applications with user interfaces but is not specialized for chatbot conversation design.

Reasoning: Power Virtual Agents is specifically designed to create chatbots with topics, triggers, and integration points for escalation and handoff to humans. It can call Power Automate flows and integrate with customer service platforms, making B the correct answer.

Question 7: 

Which Power BI feature allows business users to ask natural-language questions and instantly generate visualizations based on their queries?

A) Dataflows

B) Q&A

C) Dashboards

D) Power BI Desktop

Answer: B) Q&A

Explanation: 

A) Dataflows are designed to ingest, prepare, and transform data using Power Query in the cloud. They allow centralized transformation logic and reusable data entities that can be consumed across multiple Power BI reports. While dataflows help standardize business data, they do not provide an interface for natural-language queries or generation of instant visualizations. Their role is firmly rooted in data preparation rather than interactive user questioning or insights generation.

B) Q&A is the feature created specifically to allow business users to type questions using natural language and have Power BI automatically generate the most appropriate visualization. Q&A leverages linguistic models and semantic matching to interpret user intent. When a user types phrases like “total sales by region last year,” Q&A translates the request into a visual such as a bar chart or column chart using available report datasets. It enables rapid insights without the need for formal report building skills and enhances self-service analytics capabilities.

C) Dashboards in Power BI are collections of pinned tiles from various reports. They provide a consolidated view of key metrics and can contain charts, KPIs, graphics, or links to reports. Dashboards are useful for monitoring and decision-making but do not interpret natural-language queries or generate visuals dynamically. Users interact with dashboards by viewing tiles or drilling into reports, not by asking questions in conversational language.

D) Power BI Desktop is the primary tool for building data models, designing reports, shaping data with Power Query, and defining measures using DAX. It is intended for creators rather than end-users seeking quick answers. Power BI Desktop does not offer natural-language querying within the development environment. Instead, it focuses on modeling, transformation, and report authoring.

Reasoning: Among all the listed elements, only Q&A provides the natural-language question interface that converts user queries into ready-made visualizations. Dashboards display prebuilt visuals, Power BI Desktop builds them, and dataflows prepare data. Q&A uniquely enables instant insight through conversational input. Therefore B is correct.

Question 8: 

In Power Automate, which flow type is triggered by an event occurring in a connected service, such as a new email arriving?

A) Instant flow
B) Scheduled flow
C) Automated flow
D) Desktop flow

Answer: C) Automated flow

Explanation: 

A) Instant flow requires the user to manually trigger it. These flows start through actions such as pressing a button in the mobile app, selecting a command from the Power Automate interface, or invoking them from Power Apps. They are best for processes that need user initiation, not background automation triggered by external events. An example would be a user clicking a button to send an approval request.

B) Scheduled flow runs at predetermined intervals, such as every hour or once daily. It is useful when actions must recur at consistent times. Scheduled flows do not respond to new events occurring spontaneously in connected services. For example, regularly exporting a dataset or generating a daily report would use scheduled flows.

C) Automated flow is specifically designed to trigger automatically when an event occurs in a connected service. Examples include receiving an email in Outlook, adding a row in Excel, modifying a SharePoint list, or receiving a new message in Teams. The user defines a trigger from available connectors, and when the condition is met, Power Automate runs the flow instantly. This matches the scenario described in the question.

D) Desktop flow is part of Power Automate for desktop automation (RPA). These flows record steps on a desktop computer to automate legacy applications or repetitive actions. Desktop flows are designed for robotic process automation and do not respond to cloud-based triggers such as new emails.

Reasoning: Only automated flows begin running when an event occurs in a connected service. The question references a new email arriving, which is the classic example of an event-driven trigger. Therefore C is correct.

Question 9: 

Which Power Apps component is responsible for connecting apps to external data sources such as SharePoint, SQL Server, or Excel?

A) Connectors
B) Entities
C) Expressions
D) Controls

Answer:  A) Connectors

Explanation:  

A) Connectors are the bridge that links Power Apps to external or internal data services. They provide the API communication layer that allows the app to read and write data. Power Apps offers standard connectors such as SharePoint, SQL Server, Office 365, and premium connectors like Salesforce. Without connectors, apps would not be able to communicate with data sources outside the app itself.

B) Entities (often referred to as tables in Dataverse) are data objects within Dataverse. They store rows and columns and support business logic, relationships, and security. Entities provide structured storage but do not offer integration to external systems. They are internal to the Dataverse platform.

C) Expressions are formulas used in Power Apps to define app behavior, logic, and interactions. They are used to control events such as button clicks, text updates, or field calculations. While expressions may reference data retrieved through connectors, they themselves are not the layer responsible for establishing the link.

D) Controls are UI elements such as buttons, text inputs, galleries, and forms. Controls help users interact with the app and display data but do not establish external connections. Controls rely on connectors and formulas to populate their data sources.

Reasoning: The question specifies the component that connects apps to data sources. Only connectors perform this function, making A the correct answer.

Question 10: 

What type of analytics does Power BI primarily provide to help users make data-driven decisions?

A) Descriptive analytics
B) Operational automation
C) Conversational analytics
D) RPA-driven insights

Answer:  A) Descriptive analytics

Explanation:  

A) Descriptive analytics focuses on summarizing existing data to highlight trends, patterns, and KPIs. Power BI is built on this foundation: it turns raw data into dashboards, charts, and reports that show what has happened or what is happening. Users can visualize sales trends, compare performance across departments, analyze customer behavior, and more. Power BI supports some predictive capabilities through integration with AI visuals, but its core strength remains descriptive analysis.

B) Operational automation relates to automating business processes, which aligns with Power Automate rather than Power BI. Power Automate is the component that schedules tasks, triggers workflows, and orchestrates system actions. Power BI does not automate operational processes; its purpose is insight, not automation.

C) Conversational analytics would refer to chatbot-like capabilities, where unstructured conversations are analyzed. This would fall more under Power Virtual Agents or AI Builder components. Power BI may analyze sentiment if provided data, but it is not a conversational analytics tool.

D) RPA-driven insights come from robotic process automation (like Power Automate Desktop). RPA tools mimic human operations in software environments. They do not provide business intelligence visuals, nor does Power BI rely on RPA for its insights.

Reasoning: Power BI’s central purpose is to visualize and interpret data. It is built for descriptive analytics, making A the correct answer.

Question 11: 

Which Dataverse feature enforces data quality by limiting the type of data users can enter into a table column?

A) PowerQuery
B) Data types
C) Dashboards
D) AI Builder

Answer: B) Data types

Explanation:  

PowerQuery is widely used across Power BI, Excel, and Dataflows as a transformation engine that allows users to combine, shape, clean, and load data. It employs a graphical interface as well as M language expressions to refine datasets before they enter analytical models or storage systems. However, PowerQuery operates outside the Dataverse environment and does not function within the Dataverse table structure itself. Because of that, it does not enforce restrictions on the values that end users can enter directly into Dataverse tables. Instead, it influences how data is cleaned before being loaded but cannot control or validate real-time data entry in Dataverse. Since the question focuses on data quality enforcement at the exact moment of input, PowerQuery does not fit this requirement.

Data types are a fundamental Dataverse mechanism that dictate what type of value each table column is allowed to store. These types include text, whole numbers, floating-point numbers, date/time, lookups, choices, Boolean selections, currency, images, files, and more. By assigning a specific type to a column, Dataverse ensures that only compatible values can be submitted by users or applications. If a user attempts to enter a value that does not meet the required format, Dataverse rejects the input, thereby maintaining consistency, preventing corruption, and enforcing strict data quality. Data types are intrinsic to Dataverse’s schema design and are applied automatically whenever data is entered through forms, apps, APIs, or automated processes. They therefore serve as the foundation for enforcing structured, accurate, and predictable data entry.

Dashboards, although useful for visualizing information, analyzing trends, monitoring KPIs, and offering interactive reporting experiences, do not participate in data validation or enforcement. They sit at the reporting layer, consuming data produced within Dataverse or external sources. Because dashboards do not define or influence the structure of the underlying schema, they cannot impose restrictions on what data users submit. Their role is entirely observational and analytical rather than regulatory. As a result, dashboards play no part in ensuring that only valid inputs are stored in Dataverse tables, even though they may highlight issues or indicate inconsistencies after the data has already been entered.

AI Builder provides low-code machine-learning capabilities such as prediction, classification, object detection, and form processing. These models can enhance business processes by offering insights or automating tasks. However, AI Builder does not define schema constraints and does not validate the values entered into Dataverse columns. Even though AI Builder might classify or interpret information, it does not enforce strict rules on which values users may input into Dataverse fields during entry. Its focus is on intelligent processing rather than structural enforcement, meaning it does not ensure compliance with column requirements.

The only feature that directly enforces the type of data users can enter into Dataverse columns is data types. These data types specify the required structure for each column and reject entries that do not meet the definition, ensuring strong data quality at the point of capture. Therefore, option B is correct.

Question 12: 

Which Power Platform capability allows users to securely share applications with coworkers while maintaining data access permissions?

A) Sharing from Power Apps
B) Connection references
C) Data export
D) Power Shell admin center

Answer:  A) Sharing from Power Apps

Explanation:  

Sharing from Power Apps enables app makers to grant specific users or groups permission to use their applications. When an app is shared, the recipient receives access to the interface and functions of the app while their data permissions are still governed by Dataverse security roles or connector-level privileges. This means the user can only see or manipulate data according to the rights assigned to them, ensuring secure collaboration. Sharing can be done with individuals, Microsoft 365 groups, or security groups, and makers can choose whether recipients may also edit the app. This capability directly addresses the need to ensure app distribution does not override established data protections.

Connection references serve an entirely different purpose. They allow app and flow creators to centralize the configuration of connections used within solutions so that these connections can be easily modified or reused across environments. This helps simplify ALM processes by separating connection information from the individual components that rely on it. However, connection references do not manage sharing permissions, user access, or security enforcement for apps. They function behind the scenes in solution deployments and do not control whether applications can be shared securely with coworkers.

Data export involves functions that allow users or administrators to move or copy data out of Power Platform environments. Examples include exporting data to Excel, downloading tables, or using Data Export Service for external synchronization. While helpful for reporting, archiving, or integration purposes, data export has no relationship to granting users access to applications. It does not participate in secure sharing workflows and does not provide any capability for maintaining or enforcing user permissions within apps.

PowerShell admin tools allow administrators to manage environments, perform bulk operations, configure tenants, and automate governance tasks. These command-line tools are powerful for administrative functions but do not serve the purpose of sharing applications with end users. While they can control environment-level resources, they do not provide a user-friendly way to share apps nor do they determine how app-level permissions apply when distributing Power Apps to colleagues.

Only sharing from Power Apps enables makers to distribute applications securely while preserving underlying data permissions. It ensures that coworkers can use the app without gaining inappropriate access to data. Therefore, option A is correct.

Question 13: 

Which environment type should be used when building solutions that require governance, backups, and lifecycle management?

A) Personal environment
B) Default environment
C) Sandbox environment
D) Trial environment

Answer: C) Sandbox environment

Explanation:  

A personal environment is automatically created for certain users to support individual experimentation and limited personal development. While useful for learning or casual exploration, it lacks the governance controls, lifecycle management tools, and structured capabilities required for team-based or organizational solution development. These environments are not suited for formal ALM processes such as deploying managed solutions, promoting apps through multiple stages, or coordinating development across multiple contributors. Because personal environments are isolated to individual users and lightweight in functionality, they cannot support enterprise development requirements.

The default environment exists for the entire organization and contains basic storage for apps, flows, and tables created by users. Although it provides access to core Power Platform features, it does not offer the isolation required for structured development processes. Because everyone has access, it quickly becomes cluttered, making it inappropriate for robust solution lifecycle management. It lacks the separation needed for development, testing, and staging, and is not intended for managing managed solution deployment. Using the default environment for formal ALM introduces risks such as accidental modifications, inconsistent configurations, and a lack of clean governance.

A sandbox environment is specifically designed to support controlled application development, testing, and lifecycle management. It allows teams to perform solution imports and exports, test updates, perform backups and restores, and validate changes before pushing them to production. Sandbox environments offer the isolation required to test new features without affecting live users, making them ideal for structured ALM pipelines. Because they support advanced governance features such as security role management, auditing, and data policies, they are the correct choice when an organization requires a disciplined development process. Sandboxes can be reset, copied, and backed up, ensuring changes can be tested thoroughly before deployment.

Trial environments allow users to explore Power Platform capabilities temporarily, usually for evaluation or proof-of-concept purposes. They are automatically set to expire within a short period and are not intended for long-term development or organizational governance. Although they provide access to many features, their temporary nature makes them unsuitable for establishing a sustainable environment lifecycle or storing production-ready solutions. Because trial environments eventually expire, they cannot support backups, structured ALM, or governance needs in a consistent manner.

A sandbox environment is the only environment type designed with governance, solution lifecycle management, safe testing, and backup capabilities in mind. It provides the separation and administrative tools required for structured development. Therefore, option C is correct.

Question 14: 

Which feature in Power Automate allows a workflow to pause until a specific condition becomes true?

A) Switch
B) Do Until
C) Instant trigger
D) Parallel branch

Answer: B) Do Until

Explanation:  

A Switch action evaluates a single value and compares it against a list of matching cases. Based on the match, the workflow routes execution to the corresponding branch. This functionality is helpful for decision-making processes that branch based on specific values. However, Switch does not include any capability to pause the flow and continuously check for a changing condition. It simply selects the correct path once and proceeds. Because it does not wait or re-evaluate a condition, it is not suitable for workflows that require repeated checks before moving forward.

A Do Until action is explicitly designed to loop repeatedly until a defined condition becomes true. It allows the workflow to pause, reattempt actions, and continue checking for changes until the condition is met. During its execution, the Do Until action supports timeout settings, interval configurations, and limits to ensure efficient processing. This makes it ideal for scenarios where the workflow must wait for an external system to update a value, a process to complete, a record to reach a certain state, or any other situation where time-based or state-based waiting is necessary. Among all the options given, it is the only one with built-in waiting behavior tied to a condition becoming true.

Instant triggers initiate a flow based on user actions, such as clicking a button or selecting a message. While they allow flows to start manually, they do not provide any mechanism for pausing execution or monitoring for conditional changes during the flow run. Instant triggers simply serve as the starting mechanism and have no relationship to loops, waiting, or condition checking after a flow has begun. Therefore, they cannot satisfy the requirement of pausing until a condition is met.

Parallel branches allow segments of a flow to run at the same time. This is useful for scenarios involving tasks that do not depend on each other and can be completed simultaneously. However, parallel branches do not include waiting capability, state checking, or repeated condition evaluation. Their purpose is to execute independent actions concurrently, not to monitor and pause based on conditions.

Do Until is the only feature that enables conditional waiting by repeatedly evaluating a condition until it becomes true. Because the question specifically asks for the feature that pauses execution until a condition is met, option B is correct.

Question 15: 

Which Power Apps feature improves performance by caching data locally for repeated use?

A) Collections
B) Variables
C) Themes
D) Controls

Answer:  A) Collections

Explanation:  

Collections in Power Apps serve as temporary, in-memory storage for tabular data. They can store multiple records, represent tables, and cache results from data sources. Because collections retain data locally during an app session, they help reduce redundant data calls, minimize network traffic, and improve performance. For example, if a gallery repeatedly displays the same records, loading the data into a collection once ensures that subsequent updates rely on the cached data rather than repeatedly querying the underlying data source. Collections are versatile and can be created, modified, and cleared dynamically, making them ideal for scenarios requiring cached datasets.

Variables hold individual values or objects but do not function as cached, multi-record datasets. Power Apps includes global variables, context variables, and component variables, each serving distinct purposes. While variables are helpful for storing flags, selected items, calculated values, and user-specific temporary information, they are not built to hold tables of data for repeated use. Because they do not provide the same caching advantages as collections, they cannot improve app performance in scenarios where large or repeated data retrievals occur.

Themes control the visual appearance of the app, including color schemes, fonts, and layout styles. They enhance branding consistency and improve user interface aesthetics. However, themes have no effect on data retrieval, performance optimization, or caching behavior. While themes make apps look more professional, they do not influence how data is stored, accessed, or reused during app execution.

Controls are the visual components that make up the user interface, such as text inputs, galleries, buttons, dropdowns, and labels. They are used to display or collect information and enable user interaction. Although controls can display data, they do not store datasets or cache information for performance purposes. Their focus is on interaction rather than optimized data handling. Even if a control displays repeated information, it cannot cache or store that information without relying on underlying collections or variables.

Collections uniquely store and cache tabular data locally, reducing repeated data source calls and improving performance. None of the other options provide comparable caching functionality. Therefore, option A is correct.

Question 16: 

Which capability in Power BI allows merging multiple datasets based on matching fields?

A) Sync slicers
B) Drillthrough
C) Merge queries
D) Row-level security

Answer: C) Merge queries

Explanation:

Sync slicers is a Power BI feature designed to maintain uniform filtering across multiple report pages rather than combining or transforming datasets. When a report contains several pages, and the creator wants a specific filter, such as region or date, applied consistently across those pages, sync slicers ensures that selections remain synchronized. This capability is extremely useful in providing a smooth end-user experience because the user does not have to repeatedly select the same slicer value across multiple views. However, while sync slicers improves usability and consistency in filtering, it does nothing to combine tables, reshape data, or join datasets. Its purpose is purely report-level interaction rather than any manipulation of the underlying data sources.

Drillthrough is another interactive report feature in Power BI that helps users move from a summary page to a more detailed view. When configured, users can right-click or select an element to navigate to a page dedicated to the selected category. This enhances analytical depth by showing more granular data related to a chosen dimension. However, drillthrough works exclusively at the visualization and report navigation levels. It does not participate in merging tables, performing transformations, or joining fields from separate data sources. Even though it enables deeper insight, it still relies on already-prepared data rather than creating integrated datasets through matching columns.

Merge queries, by contrast, is a data transformation capability found in Power Query, which is used to manipulate and prepare data before loading it into a Power BI model. Merge queries allows two or more tables to be combined based on shared fields, acting much like joins in SQL. Whether it is a left join, inner join, right join, or full outer join, the feature lets users select the matching column and specify how they want the merged results to appear. This enables the construction of enriched datasets that pull together related information from different sources. Since creating comprehensive datasets is often essential for meaningful analysis, merge queries plays a central role in enabling analysts to prepare clean, unified data models.

Row-level security is a security mechanism that restricts what data certain users can see once a Power BI report is published. By configuring security roles and filters, administrators ensure that individuals only view records assigned to them, such as their region or department. This is vital for safeguarding sensitive information, maintaining privacy controls, and adhering to organizational compliance requirements. However, row-level security governs only data visibility after a model has been built; it does not combine, merge, or connect datasets during data preparation. It works on filtering at the report consumption stage, rather than shaping or merging data sources beforehand.

Reasoning: The only capability that merges datasets based on shared fields is merge queries, making option C the correct answer.

Question 17: 

Which security mechanism in Dataverse protects sensitive fields such as salary or identification numbers?

A) Environment roles
B) Field-level security
C) Solutions
D) Audit logging

Answer: B) Field-level security

Explanation:

Environment roles in Dataverse control what a user can access within the broader Power Platform environment. These roles regulate administrative rights such as creating apps, managing databases, or overseeing environment configurations. While environment roles determine high-level permissions and general access boundaries, they do not address access control at the individual field level. As a result, although they are essential for environment governance, they are ineffective for protecting specific sensitive fields like salary, national ID values, or other confidential attributes that require selective visibility.

Field-level security, however, is specifically designed for the protection of individual columns that contain sensitive or regulated information. With field-level security, administrators can define which users can read, update, or create values in highly confidential fields. This means even if a user has access to a table, they may still be restricted from viewing or modifying sensitive columns. Organizations dealing with HR information, financial compensation, or personal identification data rely heavily on field-level security to prevent unauthorized exposure. It is one of the most important data-protection capabilities in Dataverse, ensuring that information is safeguarded at the most granular level possible.

Solutions in Dataverse serve as containers for transporting components between environments, such as tables, apps, flows, and security roles. Their primary purpose is packaging and deployment, supporting the application lifecycle across development, testing, and production environments. While solutions help maintain structure, organization, and repeatability in deployment scenarios, they do not perform any security enforcement on individual fields or records. They manage objects rather than data protection, which makes them unrelated to the safeguarding of sensitive fields.

Audit logging is a feature used to track changes, record access, and maintain historical records of actions performed on data. Audit logs capture details such as who viewed a record, who updated a field, or when changes occurred. Although audit logging contributes to compliance, traceability, and security monitoring, it does not actually enforce access restrictions. It records actions after the fact rather than preventing unauthorized viewing or editing of sensitive data. Thus, audit logging cannot replace mechanisms like field-level security that ensure only approved users can access specific sensitive information.

Reasoning: The only mechanism designed specifically to restrict visibility and update rights for individual sensitive fields is field-level security, making option B correct.

Question 18: 

Which connector type requires an additional license because it integrates with high-value enterprise systems?

A) Standard connector
B) Premium connector
C) Custom connector
D) Deprecated connector

Answer: B) Premium connector

Explanation:

Standard connectors form the foundational set of connectors available in Power Apps and Power Automate without additional licensing requirements. They integrate with commonly used services such as SharePoint, Outlook, Excel Online, and basic databases or web APIs. Their purpose is to enable broad usage and accessibility across the platform, supporting a wide range of typical business workflows. While standard connectors enable robust automation and app-building, they are not associated with any special licensing tiers and therefore do not require extra payment to use beyond the general platform subscription.

Premium connectors, by contrast, enable integration with enterprise-grade systems that typically handle more complex, business-critical operations. Examples include Salesforce, ServiceNow, premium SQL connectors, SAP, and other high-value platforms. Because these systems often contain essential business data and require enhanced capabilities, Microsoft classifies them under premium licensing. Using premium connectors requires either a Power Apps per-app/per-user license or a Power Automate premium plan. The added cost reflects their advanced functionality, deeper integration requirements, and higher enterprise value.

Custom connectors allow organizations to build their own connectors to interact with internal APIs or specialized systems. While creating and deploying a custom connector may require a premium license in certain scenarios, the connector itself is not inherently premium. Its licensing depends on the data source or the plan under which it is used. Custom connectors are flexible and support tailored integration, but they are not automatically subject to the additional cost associated with premium connectors.

Deprecated connectors are outdated or retired connectors that Microsoft no longer supports. They typically remain available only for backward compatibility during transition periods. Since deprecated connectors are not intended for new development and do not represent premium functionality, they do not require additional licensing. Their classification relates to lifecycle status rather than licensing tiers or enterprise capabilities.

Reasoning: Premium connectors are the only connector type explicitly tied to additional licensing because they access enterprise-grade systems. Therefore, option B is correct.

Question 19: 

Which Power BI feature allows users to view data row by row in a tabular, drillable format?

A) Matrix visual
B) Decomposition tree
C) Gauge visual
D) KPI visual

Answer:  A) Matrix visual

Explanation:

The matrix visual is designed to display data in a tabular structure that supports rows, columns, and drill-down interactions. Users can explore hierarchical data by expanding or collapsing different levels, making the matrix one of the most effective tools for examining detailed data in a structured format. It offers flexibility similar to pivot tables in Excel, which is one reason it is widely used for operational and analytical reporting. Its drillable nature and ability to present information row by row align perfectly with scenarios where users need structured, interactive tabular analysis.

The decomposition tree visual serves a very different purpose. Rather than presenting data in rows and columns, it breaks down a selected measure into contributing categories. It is designed for root-cause exploration, allowing users to branch into different dimensions to see how each contributes to a metric. Although interactive and powerful for explanation, it is not a tabular tool and does not present data row by row. Its structure is hierarchical but not tabular, so it cannot fulfill the requirement stated in the question.

The gauge visual focuses on displaying a single metric relative to a target. It uses a needle or filled bar to indicate performance against a goal. This makes gauge visuals appropriate for dashboards involving KPIs or progress tracking. However, gauges display only one value at a time and provide no rows, columns, or drill-down options for multiple data points. Their purpose is measurement, not detailed exploration.

The KPI visual similarly displays performance indicators by comparing actual values against targets. It usually includes trend indicators, color coding, or direction symbols to show whether performance is improving or declining. While useful for executive dashboards, KPI visuals do not display data in a detailed or row-based format. They condense large amounts of information into a single visual indicator rather than offering interactive detail analysis.

Reasoning: Only the matrix visual is capable of showing data in a tabular, row-by-row format with drill-down capability, making option A the correct answer.

Question 20: 

Which Power Automate capability allows calling a flow directly from a Power Apps button?

A) Child flow
B) Business process flow
C) Power Apps trigger
D) Approval flow

Answer: C) Power Apps trigger

Explanation:

Child flows are designed to be invoked from parent flows within Power Automate. They enable modularization and reuse of automation logic so that a larger flow can delegate tasks to smaller ones. However, child flows cannot be directly triggered from Power Apps. Their role is strictly related to flow-to-flow interaction rather than app-to-flow interaction. While extremely useful for maintaining clean automation architecture, they do not enable a Power Apps button to start a flow.

Business process flows provide guided steps for users to follow within a model-driven app. These flows ensure that processes such as case management, onboarding, or sales qualification follow consistent stages. Their purpose is to enforce structured progression rather than to initiate automation from a button. They cannot be triggered directly by a Power Apps control and therefore do not satisfy the requirement described in the question.

A Power Apps trigger is a specific trigger type in Power Automate that launches a flow when a Power Apps button or event calls it. Developers can connect a button in the app to the flow, and when users interact with that button, the flow executes. This allows user-driven processes inside Power Apps to connect seamlessly to automation workflows, such as writing data, starting approvals, or integrating external systems. The Power Apps trigger is specifically built for this scenario, making it the correct capability.

Approval flows are specialized flows built for the approval process lifecycle. They collect approvals, notify approvers, log responses, and update systems accordingly. While approvals can be initiated by many types of triggers, they cannot inherently be started directly from a Power Apps button unless the flow contains a Power Apps trigger. Approval flows describe a process type, not an app-to-flow connection, so they do not meet the requirement.

Reasoning: The only capability that enables a Power Apps button to directly start a flow is the Power Apps trigger, making option C correct.