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Microsoft Agentic AI Business Solutions Architect (AB-100日本語版) Sample Questions:
ある企業は、単一のエージェントと単一のプロンプトを使用して一連のタスクを完了するMicrosoft Foundryプロジェクトを保有している。
エージェントは以下の問題に遭遇します。
不完全な結果を生み出すことが多い。
ドメイン固有の推論に苦戦する。
エージェントの応答時間が著しく遅い。
エージェントの全体的なパフォーマンスと精度を向上させるための解決策を提案する必要があります。
推奨事項には何を含めるべきでしょうか？回答するには、適切なアクションを正しい要件にドラッグしてください。各アクションは、1回、複数回、またはまったく使用しないことができます。
Correct Answer:
Explanation:
To improve performance # Move to a multi-agent architecture
To improve accuracy # Add a grounding data source
The current design uses a single agent and a single prompt to complete a series of tasks. That is often a bottleneck. When one agent is responsible for everything, it has to manage multiple steps, multiple reasoning modes, and multiple task transitions in one flow. This commonly leads to:
slower response times
task overload
incomplete outputs
reduced efficiency as complexity grows
Moving to a multi-agent architecture helps performance because tasks can be separated by function. For example:
one agent can handle task planning
another can retrieve domain knowledge
another can perform structured reasoning
another can prepare the final response
From an agentic AI systems perspective, decomposition improves throughput and execution quality. Instead of one overloaded agent trying to do everything, specialized agents handle narrower responsibilities. That often reduces latency in practical enterprise designs and improves the reliability of task completion.
This also addresses the symptom of incomplete results, because a multi-agent architecture can break a large workflow into smaller, controlled substeps.
Why "Add a grounding data source" improves accuracy
The agent struggles with domain-specific reasoning. That strongly suggests it lacks sufficient domain context during inference.
The best way to improve accuracy in this case is to add a grounding data source.
Grounding means giving the model access to trusted, relevant business knowledge at runtime, such as:
internal documentation
product specifications
policy manuals
knowledge bases
industry-specific reference data
This improves domain-specific reasoning because the model no longer relies only on general pretrained knowledge. Instead, it can anchor its responses in authoritative content.
From an AI business solutions standpoint, grounding is one of the most important mechanisms for improving:
factual relevance
domain accuracy
consistency
trustworthiness
explainability in enterprise contexts
When a model is inaccurate because it lacks business context, grounding is usually a better first fix than simply scaling model size.
Why the other actions are not the best fit
Add a prebuilt connector
A prebuilt connector helps with integration to systems and services, but it does not directly solve slow reasoning, incomplete output, or weak domain-specific reasoning unless the issue is specifically missing access to an external system. That is not the main problem described here.
Upgrade to a larger generative AI model
A larger model may sometimes improve reasoning quality, but it usually comes with higher cost and often slower response times, which works against the stated performance issue. It is not the best recommendation when the current agent is already slow.
Also, when domain-specific reasoning is the problem, grounding is usually more efficient and more controllable than simply choosing a larger model.
Expert reasoning shortcut
Use this exam logic:
Slow and overloaded single agent handling many tasks # move to multi-agent architecture Weak domain-specific reasoning # add grounding data source Need system integration # prebuilt connector Need raw generative capability increase, but can accept more cost/latency # larger model
あなたは、Microsoft Dynamics 365 カスタマーサービス担当者をサポートする Microsoft Copilot Studio エージェントを評価しています。
以下の要件を満たすテストソリューションを推奨する必要があります。
アクティブセッション中の薬剤の有効性を評価する
エージェントが正確で役立つ回答を提供しているかどうかを検証します。
継続的な改善のための、測定可能で実用的な洞察を提供する
何をおすすめしますか？
- A. 負荷テストを実施して、チャット量が多い状況下でエージェントがどのようにスケーリングするかを検証します。
- B. 稼働時間とページ読み込み時間を測定します。
- C. 過去のチケットを確認し、解決時間が最も短いエージェントを見つけます。
- D. ダッシュボードを使用して解決率、偏向、精度を追跡し、スクリプトを使用して一貫した応答を確保します。
Correct Answer: D 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
あなたはMicrosoft Copilot Studioを使用して、ローコードAIビジネスソリューションを設計しています。
このソリューションには、ボタンのクリック、テキストの入力、画面からの情報抽出など、サードパーティ製アプリやウェブサイト上でのユーザー操作をシミュレートすることでタスクを自動化するエージェントが含まれている必要があります。
エージェントに何を含めるべきかを推奨する必要があります。
何をおすすめしますか？
- A. Copilot Studioにおける自然言語理解+(NLU+)モデル
- B. 副操縦士のスキル
- C. Copilot Studioでのコンピュータの使用
- D. モデルコンテキストプロトコル(MCP)
Correct Answer: C 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
ある企業は、販売取引の生成を支援する複数のAIモデルを保有している。
モデルの各リリースは、本番環境に展開する前に、セキュリティおよびコンプライアンスチームによるレビューを受けなければなりません。セキュリティおよびコンプライアンスチームは、潜在的な脆弱性を適切に判断するために、以前のバージョンにアクセスできる必要があります。
各デプロイメントが本番環境に与える影響を評価するためのソリューションを提案する必要があります。そのソリューションは、事業継続性を強化するものでなければなりません。
何をおすすめしますか？
- A. バージョン履歴を使用する中央モデルレジストリを作成します。
- B. サービスの中断を防ぐため、モデルの廃止スケジュールを追跡します。
- C. 品質ゲートを使用して昇進プロセスを確立する。
- D. すべてのAIシステムコンポーネントにバージョン管理を実装する。
Correct Answer: D 🗳️
Explanation: Only visible for DumpExam members. You can sign-up / login (it's free).
ある企業は、クラウドデータベースに保存されている顧客からのフィードバックのレビューを自動化するAIエージェントを導入している。
エージェントの出力データから月次レポートを作成し、顧客の感情に関する洞察を得て、製品開発とマーケティングの指針とする計画です。
エージェントが取り込むデータがクリーンで、意図された用途に適していることを確認する必要があります。
データの準備には何をすべきですか？
- A. データベースのサイズが100GBを超えないようにしてください。
- B. 顧客の姓でデータベースを並べ替えます。
- C. 偏ったデータを特定し、対処する。
- D. データを単一の言語に翻訳する。
Correct Answer: C 🗳️
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