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Last Updated: Oct 14, 2026
No. of Questions: 159 Questions & Answers with Testing Engine
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| Section | Weight | Objectives |
|---|---|---|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement generative AI and agentic solutions | 30–35% | - Build generative AI applications
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement computer vision solutions | 10–15% | - Implement image analysis and processing
|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
あなたは、Azure AI を使用してユーザーが性的に露骨な画像を共有することを防止する画像共有アプリを開発しています。
不適切な画像が正しく識別されるようにする必要があります。また、開発の手間を最小限に抑えるソリューションでなければなりません。
何を使うべきでしょうか?
Correct Answer: D 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
注:このセクションには、同じシナリオと問題に関する複数の質問セットが含まれています。各質問には、問題に対する固有の解決策が提示されています。提示された解決策が、提示された目標を満たしているかどうかを判断する必要があります。セット内の複数の解決策が問題を解決できる場合もあります。また、セット内のどの解決策も問題を解決できない場合もあります。
このセクションの質問に回答すると、前のセクションに戻ることはできません。そのため、これらの質問は復習画面には表示されません。
画像のアップロードを受け付け、抽出した画像テキストを使用して応答を生成する、マルチモーダルなAI生成モデルをお持ちです。
ユーザーが安全でない画像をアップロードしたり、モデルを操作するための隠された指示を画像に埋め込んだりできることが分かります。
リスクを軽減するための対策を実施する必要があります。
解決策:保護対象物の検出設定を行います。
これは目標を達成していると言えるでしょうか?
Correct Answer: B 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
エージェントを含む Microsoft Foundry プロジェクトがあります。
エージェントは、表と埋め込まれたQRコードを含む、スキャンされたPDF形式の仕入先請求書を取り込みます。
エージェントは、抽出された出力においてPDFのレイアウトを保持し、後続の処理でセクションや表を参照できるようにする必要があります。
Foundry Tools で Azure Content Understanding を呼び出す予定です。
言語モデルの展開を必要とせずに、コンテンツとレイアウト要素を抽出し、QRコードを検出する必要があります。
どの内蔵アナライザーを使用すべきですか?
Correct Answer: A 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
あなたはAzureサブスクリプションをお持ちです。
あなたは、Azure OpenAI DALL-Eモデルを使用するアプリを開発する予定です。
モデルをデプロイする必要があります。
何を使うべきでしょうか?
Correct Answer: B 🗳️
Explanation: Only visible for Prep4King members. You can sign-up / login (it's free).
ホットスポットに関する質問
あなたは画像内の物体を検出するモデルを構築しています。
訓練データに基づくモデルの性能を以下の図に示します。
図に示された情報に基づいて、各記述を完成させる選択肢をドロップダウンメニューを使用して選択してください。
注:正解ごとに1ポイントが加算されます。
Correct Answer:

Explanation:
Box 1: 0
The percentage of false positives is 0%.
Because the model made zero incorrect positive predictions, the count and percentage of false positives must be exactly zero.
Box 2: 25
The value for the number of true positives divided by the total number of true positives and false negatives is 25% (or 0.25).
High Precision (100%): Every single object your model detects is correct; it generates zero false alarms.
Low Recall (25%): Your model misses 75% of the actual objects it was supposed to find.
'
Reference:
https://medium.com/grabngoinfo/how-to-evaluate-the-performance-of-a-binary-classification-model-6e7193dcbbf9
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