AB-620 Study Guide: Designing and Building Integrated AI Agent Solutions in Microsoft Copilot Studio

Certification: Microsoft Certified: AI Agent Builder
Associate
Exam: AB-620
Last reviewed: July 28, 2026

AB-620 is an integration and solution-design exam. It is not enough
to know how to create a basic Copilot Studio agent or where a setting
appears in the maker interface. Candidates need to understand how an
enterprise agent should be planned, secured, grounded, extended, tested,
monitored, reused, and deployed.

Start Exam prepration with Free AB-620 Practice Questions.

AB-620 exam at a glance

Microsoft divides the exam into three domains:

The second domain carries the highest weight, but the domains
overlap. An integration decision can also involve identity, governance,
testing, and deployment. Study the platform as a connected system
instead of treating each objective as a separate list of facts.

Microsoft’s exam sandbox is useful for learning the testing interface
and interacting with different question styles. The live exam can
change, so use the sandbox for interface familiarity rather than as
evidence of an exact question count.

What AB-620 is really testing

Most difficult items are built around a design decision. Several technologies might technically work, but only one fits the stated requirements. The correct approach normally satisfies the functional need while also respecting security, maintainability, traceability,
governance, and deployment constraints.

Learn to recognize these common requirement signals:

The table is a study aid, not a replacement for understanding. A
scenario may combine several of these needs. For example, an agent might
use a knowledge source for policy guidance, an authenticated tool for
record updates, a specialist agent for analytics, and a human approval
step before a high-impact action.

Domain 1: Plan and configure agent solutions Begin with an architecture, not a topic canvas

A strong agent design starts with seven decisions:

1. Audience: Is the agent for employees, customers,
   partners, or more than one group?
2. Channels: Will users reach it through Teams,
   Microsoft 365 Copilot, a website, a mobile app, or a custom
   application?
3. Identity: Which interactions can be anonymous, and
   which require a signed-in user?
4. Knowledge: Which approved information sources
   should ground the agent’s answers?
5. Actions: Which systems must the agent read from or
   update?
6. Governance: Which data policies, safety controls,
   audit requirements, and human approvals apply?
7. Lifecycle: How will the solution be tested,
   versioned, promoted, monitored, and maintained?

These decisions affect one another. A public website agent can
provide anonymous access to general information, but a request involving
a user’s account, case, order, or personal record normally requires
authentication. An internal Teams agent can use Microsoft Entra ID
identity, but the connected knowledge source and tools must still be
configured to respect the user’s actual permissions.

Avoid designs that silently rely on the maker’s identity for end-user
operations. Shared or maker-provided credentials might be acceptable for
narrowly controlled system operations, but they are a poor fit when
actions must be attributable to the individual user or when different
users have different data permissions. The exam frequently rewards
least-privilege designs and clear identity boundaries.

Understand authentication and permission trimming

Authentication proves who the user is. Authorization determines what
that user is allowed to access or do. AB-620 scenarios often require
both.

For Teams and Microsoft 365 scenarios, Microsoft Entra ID can provide
a natural signed-in experience. Other channels can use manually
configured authentication when the agent needs an access token or must
work with another OAuth 2.0 identity provider. When a knowledge source
uses end-user authentication, the agent should return only content the
current user is permitted to see.

Keep the following distinctions clear:

Authentication does not replace data policy. Power Platform data
policies can classify connectors as business, non-business, or blocked
and prevent unsafe movement of organizational data. Treat Data Loss
Prevention controls as enforced architecture, not as documentation or a
recommendation.

Plan Responsible AI controls as a system

Responsible AI is broader than adding a disclaimer. A complete design
considers the model, grounding sources, instructions, content
moderation, identity, tool permissions, human oversight, evaluation,
logging, and the effect of the agent on people and business
processes.

A useful layered model is:

• Grounding: Prefer approved sources when factual
  accuracy and traceability matter.
• Instructions: Define role, tone, boundaries,
  refusal behavior, and required response conventions.
• Content safety: Apply moderation appropriate to the
  use case and investigate when valid content is unexpectedly
  filtered.
• Tool governance: Restrict what the agent can call
  and which data can cross connector boundaries.
• Human oversight: Require review or approval for
  high-impact, ambiguous, or irreversible actions.
• Testing and monitoring: Test both normal and
  adversarial inputs, then review real usage and failures after
  deployment.

A global rule should normally be configured centrally rather than
copied into every topic. Repeated manual text creates drift and makes
later updates difficult. Topic-level instructions are still useful when
one business process needs special formatting, a narrower knowledge
boundary, or a different style from the rest of the agent.

Treat knowledge, tools, and agents as different building blocks

Many AB-620 mistakes come from using the wrong class of
capability.

A document library should not be treated as a transactional tool. A
connector action should not be treated as an enterprise knowledge index.
A remote agent should not be reduced to a basic HTTP call when the
requirement is true agent-to-agent collaboration.

Configure knowledge grounding deliberately

Copilot Studio can use knowledge at the agent level and within a
generative answers node in a topic. The difference is scope.

Agent-level knowledge is broadly available to the agent. Topic-level
generative answers can use a more focused set of sources for a
particular intent. When a sensitive or specialized topic should consult
only approved sources, use the option that searches only the selected
sources. Do not assume the node will automatically fall back to every
agent-level source when that restriction is enabled.

Also understand the effect of ungrounded responses. Allowing general
model knowledge can make an agent more flexible, but it reduces control.
In a regulated policy, financial, legal, or operational scenario, the
safer design may be to block a generated answer when no approved source
or tool was used and then trigger fallback or escalation.

SharePoint and Dataverse

These are natural choices for Microsoft-based internal content and
data. The important exam concept is permission-aware retrieval. When
configured with user authentication, the agent should respect what the
current user can access rather than exposing all content available to
the maker.

Copilot connectors

Copilot connectors are useful when an organization needs to index
content from external enterprise systems into Microsoft Search and make
that content available as enterprise knowledge. They fit read-oriented
grounding scenarios and can preserve source permissions. Do not confuse
a Copilot connector used as knowledge with a Power Platform connector
action used to perform an operation.

Azure AI Search

Azure AI Search is a strong option when the organization already has
a curated search index, needs vector or semantic retrieval, wants
centralized control over indexed content, or requires traceable
citations. Candidates should understand the main configuration elements:
the search service connection, authentication method, endpoint, index,
and citation-supporting metadata.

A citation is only useful when it points to a source the user can
open. The indexed data should include a usable URL field, and users must
have permission to reach the referenced content. During testing, inspect
which sources were retrieved rather than judging only the fluency of the
final response.

Use topics for controlled conversational processes

A topic is suitable when the agent needs a predictable conversational
path, explicit branching, variable handling, validation, a defined tool
call, or structured collection of user input.

Key topic elements include:

• Message nodes for communicating information.
• Question nodes for collecting a value.
• Conditions for branching based on user input or
  variables.
• Variables for carrying data through the
  conversation and into tools or flows.
• Adaptive Cards for structured display and
  input.
• Tool or action nodes for explicitly invoking an
  operation.
• Generative answers nodes for topic-scoped grounded
  responses.

Do not add a tool only because it exists. Its name and description
should make clear what it does and when it should be selected. Poorly
differentiated descriptions can cause generative orchestration to select
the wrong tool or agent.

Separate global instructions, topic instructions, and prompts

These concepts are related but not interchangeable.

A prompt can transform content, but it does not automatically provide
trustworthy enterprise data. Grounding and prompting solve different
problems. Grounding supplies relevant evidence; instructions and prompts
shape how the model uses or transforms that evidence.

Build agent flows as reliable business operations

Agent flows are appropriate for reusable, multi-step operations that
may involve connectors, data processing, branching, approvals, or calls
to other AI capabilities. They can be packaged in solutions and
versioned as part of the wider application lifecycle.

A flow designed for an agent should have a clear contract:

• Inputs have meaningful names and expected data types.
• Outputs return only the values the agent needs.
• Authentication and connection ownership are intentional.
• Errors do not cause a silent failure.
• Retries are suitable for the external service and do not create
  duplicate transactions.
• A timeout has a defined user experience.
• High-impact actions can pause for human approval or additional
  information.
• Run history provides enough detail for troubleshooting.

Human-in-the-loop design

Human involvement is not only an escalation at the end of a failed
conversation. It can be part of the process itself. Examples include
approving an exception, reviewing a high-value transaction, supplying
missing information, or confirming an action before it changes a
record.

The agent should preserve relevant context so the reviewer does not
have to reconstruct the conversation. It should also avoid including
unnecessary personal or sensitive information in notifications or
handoffs.

Flow error handling

A reliable flow anticipates failure. Use scoped error-handling
patterns, run-after conditions, retry policies, and notifications where
appropriate. A terminate action can stop execution, so place
notification and cleanup logic where it will still run. For transient
errors, a retry may help; for validation errors or potentially
duplicative transactions, retrying blindly may make the situation
worse.

When diagnosing a problem, inspect the run history and the failed
action’s inputs, outputs, status, and error details. Monitoring the
conversation alone may show that a tool failed, but the flow history
usually explains why.

Reuse components without creating uncontrolled copies

Component collections help teams reuse topics, knowledge, tools,
child agents, MCP integrations, connectors, flows, and other agent
components. They are valuable when several agents need the same
controlled building blocks and only a platform team should edit the
shared definition.

A component collection is not the same as a solution:

• Component collection: Shares reusable agent
  building blocks among agents and can control who edits the shared
  assets.
• Solution: Packages components for lifecycle
  management and transport between environments.

A component collection can itself be exported and imported through
solutions. This allows reuse and ALM to work together.

Domain 2: Integrate and extend agents in Copilot Studio

This is the largest exam domain. Expect scenarios in which several
integration mechanisms appear reasonable. Focus on the nature of the
target system, the required authentication, whether the operation is
read-only or transactional, the need for reuse, and whether the remote
capability is an API, a tool server, a user interface, or another
agent.

Select the righ knowledge integration

A common design is to use different sources for different intents.
Policy guidance might come from Azure AI Search or SharePoint, current
operational metrics from a data agent, and transactions from
authenticated tools.

Select the right tool or action integration Power Platform connectors

Use a prebuilt connector when the target service is already supported
and its available actions meet the requirement. Connectors can be called
directly as tools or used inside agent flows. Review each connector’s
authentication model and data-policy classification.

Custom connectors

A custom connector wraps a REST API so it can be used consistently
across Copilot Studio, Power Automate, and Power Apps. It is a strong
choice when an organization has a stable internal or third-party API,
needs reusable operations, and wants a defined schema and connection
experience.

The API can be described through an OpenAPI definition or Postman
collection. Good operation names, descriptions, inputs, and outputs
matter because the agent uses metadata to decide when and how to call
the tool.

REST API tools and HTTP requests

Direct REST integration is useful when the agent needs to invoke an
HTTP service and the API can be described with the required schema and
authentication. A Send HTTP request node can fit a focused call inside a
topic, while a reusable custom connector or agent flow is usually better
when the same integration is used broadly, includes several operations,
or requires more substantial processing and error handling.

As of this guide’s review date, Microsoft labels the direct REST API
tool documentation as preview. The official AB-620 guide notes that exam
items mainly cover generally available features but can include commonly
used preview capabilities. Review the current product status close to
your exam date.

Agent flows

Use an agent flow when the operation has multiple steps, uses several
connectors, needs branching, transforms data, requires approval, or
should be reused as a business process. Map the flow’s inputs and
outputs clearly so the topic or orchestrator can pass and receive
structured values.

Model Context Protocol

MCP connects an agent to a server that publishes tools and resources
through a standard protocol. The server describes names, inputs,
outputs, and other metadata. Copilot Studio can reflect updates to the
server’s available tools and resources, making MCP useful for a
centrally maintained capability catalog.

MCP requires generative orchestration. Authentication should be
selected for each server’s security model. One MCP server might require
end-user OAuth for traceable actions, while another read-only server
might use a shared API key. Do not force every server into the same
authentication pattern.

MCP is not the same as A2A. MCP exposes tools and resources to an
agent. A2A delegates work to another agent.

Computer use

Computer use lets an agent operate a Windows desktop or website
through the graphical interface. It is designed for systems that a
person can use but that do not provide a suitable API. The agent can
select controls, enter text, and navigate screens through a configured
machine.

Use computer use only when the requirement genuinely involves UI
automation. An available API or connector is generally easier to secure,
test, and maintain. Also study where the automation runs, which
credentials it uses, how a maker can inspect execution, and when human
supervision is required.

A compact integration decision table

Design multi-agent solutions carefully

Multi-agent design is useful when specialist responsibilities need
separate instructions, tools, knowledge, security, ownership, or
deployment. It is not automatically better than one well-designed agent.
Every additional agent adds orchestration, testing, latency, and
governance considerations.

Child agents

A child agent is useful for a focused capability that belongs within
the same overall solution. It works well when one team owns the complete
system and the child does not need separate publishing, authentication,
or lifecycle management.

Connected Copilot Studio agents

Use a connected agent when the specialist already exists as a
separately managed Copilot Studio agent, needs independent publishing,
or is reused by several primary agents. The target agent must be
configured so other agents can connect to it, and its description must
clearly state when it should receive work.

Microsoft Foundry agents

A Foundry agent can provide a specialized AI capability developed
outside the primary Copilot Studio agent. It may be appropriate for
advanced processing, custom models, or centrally managed AI services.
Study the connection details, authentication, and division of
responsibility between the orchestrating agent and the specialist.

Fabric data agents

A Fabric data agent is designed for governed data questions. It is a
better fit for current metrics, semantic models, and analytical queries
than a document knowledge source or general summarization agent. Keep
current preview status and evaluation limitations in mind by checking
Microsoft documentation before the exam.

Agent2Agent protocol

A2A is an open standard for communication between agents. It supports
delegating a task to an external agent and receiving a structured
response with agent-oriented context. Use it when the remote capability
is a genuine agent with its own reasoning or workflows, particularly
when it is hosted outside Copilot Studio.

An A2A connection is not simply an HTTP connector with a different
name. It is designed for agent collaboration and can carry richer
context. The connection still needs an endpoint, accurate metadata,
appropriate authentication, observability, permission boundaries, and
careful testing.

Use orchestration metadata as part of the design

Generative orchestration relies heavily on names and descriptions.
When several tools or agents have similar descriptions, the orchestrator
can route incorrectly. Write metadata that states:

• The capability’s purpose.
• The conditions under which it should be used.
• The data or actions it supports.
• Important exclusions.
• Expected inputs and outputs.

For a complex solution, do not depend only on broad descriptions. A
topic can explicitly call a tool or redirect to another agent when the
business process requires deterministic routing.

Understand Azure-related integration points

The official outline includes three Azure-focused areas:

1. Azure AI Search with Microsoft Foundry: Ground
   answers in an enterprise search index and support traceability.
2. Foundry model catalog: Select approved models for
   prompt-driven tasks while considering organizational governance.
3. Application Insights: Collect runtime telemetry for
   diagnosis and operational monitoring.

Do not confuse observability with quality evaluation. Application
Insights helps investigate runtime behavior and technical telemetry.
Copilot Studio analytics summarizes usage and conversational
performance. Agent evaluation runs structured test sets against expected
standards. Flow run history diagnoses the execution of a particular
flow. Each tool answers a different operational question.

Domain 3: Test and manage agents Use both test chat and agent evaluation

Test chat and evaluation are complementary.

A polished response in one test chat is not proof of consistent
performance. Generative systems can produce different results across
runs. A good test set includes common requests, difficult variations,
permission-sensitive cases, ambiguous wording, unsupported requests,
failure paths, and high-risk actions.

Build useful test sets

Each test case should have a clear purpose. Depending on the
evaluation method, it can include an expected response or another
standard of quality. Curated test sets should represent real business
requirements rather than only easy examples.

A practical evaluation cycle is:

1. Define success criteria before changing the agent.
2. Build a baseline test set.
3. Run the evaluation and review both aggregate and individual
   results.
4. Inspect the transcript, activity map, selected sources, tools, and
   agents for failed cases.
5. Change one part of the design where possible.
6. Run the same set again.
7. Compare results and watch for regressions in unrelated
   capabilities.

A similarity score is not an exact-text requirement. A generated
answer can express the correct meaning with different wording. Interpret
the configured threshold, the grader explanation, and the business
importance of the case. Repeated failure is stronger evidence of a
systematic issue than one isolated result.

Automated evaluation measures defined aspects of correctness and
quality. It does not replace Responsible AI review, adversarial testing,
content safety, security testing, or human judgment.

Troubleshoot at the correct layer

Start with the symptom, then move to the layer that can explain
it.

Do not use Application Insights as a replacement for a DLP policy,
and do not use a DLP policy as a diagnostic tool. Governance blocks or
permits behavior; monitoring records and explains behavior.

Implement a disciplined ALM strategy

Microsoft’s recommended pattern uses separate Development, Test, and
Production environments. Build and modify in Development, validate in
Test, and deploy approved versions to Production.

Solutions

Solutions are the transport container for agents and their related
Power Platform components. Work in a custom solution rather than
building important production assets only in the default solution.

Use unmanaged solutions in development, where makers need to edit
components. Export and deploy managed solutions to downstream
environments unless the target is another development environment.
Managed deployment reduces uncontrolled editing and supports a
predictable release process.

Environment variables

Environment variables separate configuration from the components that
consume it. The agent and flow logic can remain the same while the API
endpoint, SharePoint site, key, data source, or other value changes
between environments.

Know the distinction between a definition and its
environment-specific value. Also know that sensitive values should use
an appropriate secret design rather than being hard-coded into topics,
flows, or connector metadata.

Connection references

Connection references associate solution-aware components with the
correct connections in each environment. They are related to environment
variables but solve a different problem. An environment variable stores
configurable values; a connection reference points the component to a
connection.

Power Platform Pipelines

Pipelines provide a governed sequence for deploying solutions between
linked environments. They can prevalidate dependencies, prompt for
connections and environment-variable values, centralize deployment
visibility, and support approval-based release controls.

A correct deployment sequence respects dependencies. Publish the
agent where required, configure its target channel or integration,
retrieve the current IDs and configuration values, test the application
integration, and only then promote the complete solution to
production.

Post-deployment
configuration

Not every Copilot Studio setting moves automatically with a solution.
Microsoft’s ALM guidance highlights items such as Application Insights
settings, some authentication settings, Direct Line or web security
settings, deployed channels, and sharing as possible post-deployment
work. Build these checks into a release checklist instead of assuming
import alone completes production readiness.

High-value distinctions to memorize

Hands-on preparation plan

Reading documentation is necessary, but AB-620 becomes much easier
after building and troubleshooting several small solutions.

Lab 1: Permission-aware knowledge agent

Create an internal agent with a SharePoint or Dataverse knowledge
source. Configure authentication, test with users who have different
permissions, inspect citations, and create one topic whose generative
answers node searches only selected sources.

Skills reinforced: identity, knowledge grounding,
permissions, citations, topic-level source scope, fallback behavior.

Lab 2: Structured topic and adaptive card

Build a topic that collects several values through an Adaptive Card,
stores them in variables, validates the input, and displays a structured
confirmation before performing an action.

Skills reinforced: topics, variables, adaptive
cards, conditions, response formatting, deterministic control.

Lab 3: Agent flow with failure handling

Create an agent flow that receives inputs, calls a connector,
transforms the result, and returns structured outputs. Add a timeout or
failure path, a notification, a suitable retry policy, and a human
approval before a high-impact step.

Skills reinforced: flow contracts, connectors, human
in the loop, run-after, retries, outputs, run history.

Lab 4: API integration comparison

Use a simple API in two ways: once through a custom connector and
once through a direct REST or HTTP approach. Compare setup,
authentication, reuse, descriptions, inputs, outputs, and
troubleshooting.

Skills reinforced: custom connectors, REST, HTTP,
OpenAPI, tool metadata, connection design.

Lab 5: MCP or computer-use integration

Connect an MCP server in a lab environment and inspect the tools and
resources it exposes. Separately, configure a small computer-use task
against a test application with no API.

Skills reinforced: MCP metadata and authentication,
generative orchestration, computer-use instructions, machine and
credential configuration, monitoring.

Lab 6: Multi-agent routing

Create a primary agent and at least one specialist. Give the
specialist a narrow role and a distinct description. Test automatic
delegation and an explicit redirect from a topic. Record cases where the
wrong capability is chosen and improve the metadata.

Skills reinforced: child or connected agents,
orchestration, descriptions, routing, inputs and outputs, latency and
governance tradeoffs.

Lab 7: Evaluation and regression testing

Create a test set with normal, edge, and permission-sensitive
interactions. Run it more than once, review individual transcripts and
scores, make a controlled change, and compare the results.

Skills reinforced: test sets, evaluation methods,
thresholds, repeated runs, regression analysis, result
interpretation.

Lab 8: Dev-to-Test deployment

Place the agent, flows, variables, and connection references in a
custom solution. Export from Development, import into Test, provide
environment-specific values, reconnect dependencies, and complete the
post-deployment checklist.

Skills reinforced: solutions, managed and unmanaged
packages, environment variables, connection references, pipelines,
dependencies, post-deployment configuration.

Four-week AB-620 study schedule

Week 1: Architecture, identity, knowledge, and topics

Read the current skills outline first. Learn the differences among
internal and external agents, anonymous and authenticated access,
grounding and general model knowledge, and agent-level versus
topic-level configuration. Complete Labs 1 and 2.

At the end of the week, you should be able to draw an agent
architecture that shows channels, identities, knowledge sources, tools,
and governance boundaries.

Week 2: Tools, APIs, flows, and reliability

Focus on Power Platform connectors, custom connectors, REST and HTTP
integration, agent flows, human approval, error handling, and
monitoring. Complete Labs 3 and 4.

At the end of the week, you should be able to choose an integration
mechanism from a business requirement and explain why the other choices
are weaker.

Week 3: MCP, computer use, multi-agent design, and Azure integration

Study MCP, computer use, child agents, connected agents, Foundry
agents, Fabric data agents, A2A, Azure AI Search, model selection, and
Application Insights. Complete Labs 5 and 6.

At the end of the week, you should be able to distinguish a tool
server from another agent and explain the operational cost of adding
more agents.

Week 4: Evaluation, troubleshooting, ALM, and exam practice

Complete Labs 7 and 8. Review test chat, evaluation, analytics,
telemetry, run history, solutions, environment variables, connection
references, managed deployments, pipelines, and post-deployment
settings.

Use the supplied practice set as a diagnostic tool. Do not memorize
its wording. For every mistake, write down the underlying distinction
that was unclear. Revisit the official documentation for that capability
and reproduce the scenario in a lab where possible.

Exam-day approach

Start each scenario by identifying the non-negotiable requirements.
Mark words related to identity, real-time data, citations, user
permissions, no-code or no-API constraints, reuse, deployment
environments, and human approval. These details often eliminate several
plausible options.

For drag-and-drop items, classify each requirement before arranging
the choices. Decide whether it concerns knowledge, action, identity,
agent delegation, testing, or ALM. Then determine sequence only where
order matters.

For multiple-select items, confirm that every selected action is
independently necessary. Do not choose an option merely because it is
generally a good practice. It must contribute to the stated
solution.

For hotspot or image-based items, read the surrounding labels and
current configuration before selecting a value. A technically valid
setting can still be wrong because the scenario uses a different
authentication model, source scope, or environment.

For case studies, create a brief mental map:

• Users and channels
• Protected and public information
• Knowledge sources
• Transaction systems
• Existing agents or tools
• Governance constraints
• Success and monitoring requirements
• Deployment environments

Finally, review marked items with the full requirement in mind. Do
not change an answer only because another option sounds more advanced.
AB-620 usually rewards the simplest supported design that meets all
requirements securely and maintainably.

Official Microsoft resources for AB-620 preparation

All links below point to official Microsoft resources.

Exam and certification pages

• Microsoft
  Certified: AI Agent Builder Associate
• Official
  AB-620 study guide and skills measured
• Microsoft certification
  exam sandbox
• AB-620T00
  course page

Core Copilot Studio documentation

• Microsoft
  Copilot Studio documentation hub
• Create
  and edit topics
• Knowledge
  sources summary
• Add
  a generative answers node
• Add
  Azure AI Search as a knowledge source
• Configure
  user authentication
• Configure
  data policies for agents
• Responsible
  AI information for Copilot Studio
• Adaptive
  Cards overview

Flows, tools, and external integrations

• Agent
  flows overview
• Use
  agent flows with an agent
• Request
  information from a human reviewer
• Add
  tools to an agent
• Use
  connectors in Copilot Studio
• Custom
  connectors overview
• Add
  a REST API tool
• Model
  Context Protocol in Copilot Studio
• Connect
  an existing MCP server
• Computer
  use in Copilot Studio

Multi-agent and Microsoft Foundry resources

• Add
  other agents overview
• Multi-agent
  solution patterns
• Connect
  through the Agent2Agent protocol
• Connect
  a Microsoft Foundry agent
• Microsoft
  Foundry documentation
• Use
  a Fabric data agent with Copilot Studio

Testing, evaluation, and lifecycle management

• About
  agent evaluation
• Create
  a test set
• Choose
  evaluation methods
• Run
  evaluations and review results
• Copilot
  Studio ALM strategy
• Create
  and manage solutions
• Export
  and import agents through solutions
• Environment
  variables in Power Platform solutions
• Power
  Platform Pipelines
• Create
  and share reusable component collections

Final preparation advice

Use the official skills outline as the boundary of your study plan
and the documentation as the source of truth. Product terminology and
preview status can change, so review the AB-620 study guide and the
relevant product pages again during the week before your exam.

The most useful preparation is a combination of three things:
understand the decision patterns, build the capabilities in a lab, and
use practice material to reveal weak areas. Candidates who can explain
why one integration, identity model, testing method, or deployment
approach fits better than another are in a much stronger position than
candidates who only memorize interface steps.