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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 2: Implement agentic solutions | 20-25% | - Build AI agents
|
| Topic 3: Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Topic 4: Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
| Topic 5: Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
Question 1
RAGアプリケーションの継続的評価に、自動接地チェックを追加する必要があります。
チェック結果は合格か不合格かの単純な二択で、別途審査モデルを導入する必要があってはなりません。どの評価モデルを使用すべきでしょうか?
A. 関連性
B. 流暢さ
C. グラウンディングプロ
D. 地に足の着いた感覚(1~5点)
Question 2
あなたはMicrosoft Foundryプロジェクトを持っています。
エージェントを含む顧客サポートソリューションを構築する予定です。このソリューションは、以下の要件を満たす必要があります。
- 内部情報に基づいた、正確で状況に応じた回答を提供する
製品ドキュメントはAzure AI Searchに保存されています。
・長文の文脈にわたる、深く多段階的な推論を必要とする。
- 詳細な自然言語による応答を生成します。
エージェントを動かすために、どのタイプのモデルを使用すべきでしょうか?
A. 大規模言語モデル(LLM)
B. マルチモーダルモデル
C. キーワード抽出モデル
D. 小型言語モデル(SLM)
Question 3
あなたはチャットボットを構築しています。
チャットボットがユーザー入力を個別のカテゴリに分類できることを確認する必要があります。カテゴリは動的であり、推論時に定義されなければなりません。
入力データの分類には、どのサービスを利用すべきですか?
A. Azure AI言語カスタムテキスト分類
B. Azure OpenAI テキスト分類
C. Azure OpenAI テキスト要約
D. Azure AI 言語のカスタム固有表現認識 (NER)
Question 4
あなたは、テキストプロンプトから製品マーケティング用の画像を生成するMicrosoft Foundryプロジェクトを持っています。
複数の画像を公開した後、貴社の法務チームは、ある画像の背景にある看板に競合他社のロゴが写っていることを発見した。
ロゴだけを削除し、画像の残りの部分はそのまま残してください。
あなたはどうすべきでしょうか?
A. 別の乱数シードを使用してプロンプトを再実行します。
B. プロンプトのガイダンス強度を高める。
C. 元のプロンプトを修正して、ブランド名を除外します。
D. ロゴが含まれている画像部分にマスクベースのインペインティング編集を適用します。
Question 5
Agent1という名前のカスタムエージェントが作成されました。
Microsoft Foundryを使用して、Agent1へのアクセスを制御し、アクティビティを監視する必要があります。
まず最初に何をすべきでしょうか?
A. Microsoft Foundry プレイグラウンドに Agent1 を追加します。
B. Microsoft Foundry プロジェクトを作成します。
C. Microsoft Foundry プロジェクトに Agent1 を追加します。
D. Application Insights リソースをプロビジョニングします。
Solutions:
| Question 1 Answer: C | Question 2 Answer: A | Question 3 Answer: B | Question 4 Answer: D | Question 5 Answer: B |
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