無料でクラウドストレージから最新のFast2test AB-731 PDFダンプをダウンロードする：https://drive.google.com/open?id=1zttGBUPl_aaQ8OwhJBDhIIqiCRz4HIOF
いつもあなたに最高のAB-731認定試験に関連する試験参考書を与えられるために、Fast2testは常に問題集の質を改善し、ずっと最新の試験のシラバスに応じて問題集を更新しています。現在の市場では、Fast2testはあなたの最もよい選択です。長い間にわたって、Fast2testは多くの受験生に認可されました。私を信じていないなら、周りの人々に聞いてみてもいいです。Fast2testの試験問題集を利用したことがある人がきっといますから。Fast2testは最優秀な試験AB-731参考書を提供してあなたを試験に合格させることを保証します。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify an implementation and adoption strategy for Microsoft AI apps and services | 20–25% | - AI adoption strategy
|
| Topic 2: Identify the business value of generative AI solutions | 35–40% | - AI value identification
|
| Topic 3: Identify benefits, capabilities, and opportunities for Microsoft AI apps and services | 35–40% | - Microsoft AI ecosystem
|
Fast2testテストトレントを学習し、試験の準備をするのに20〜30時間しかかかりません。 AB-731試験問題を購入した後、AB-731試験トレントを学習し、主に仕事、家庭生活、学習に専念するために数時間を費やすだけです。 AB-731試験問題の回答と質問は入念に選択され、試験の焦点をつかむため、試験の学習と準備に多くの時間を節約できます。合格率は98％以上と高いため、AB-731ガイドトレントを購入することで安心できます。
質問 # 74
Select the answer that correctly completes the sentence.
When a generative AI model produces output that seems realistic but contains incorrect information, the behavior is known as __________.
正解：
解説：
Explanation:
model inaccuracy
The scenario describes a model producing plausible-sounding content that is factually wrong -a common generative AI failure mode often referred to as a "hallucination." Since "hallucination" is not offered in the dropdown, the best matching choice is model inaccuracy because the core problem is that the model's output is incorrect even though it appears confident and coherent.
The other options do not fit the definition of the behavior: data leakage is about sensitive information being exposed (for example, proprietary prompts, secrets, or personal data). Prompt injection is an attack technique where a user tries to override system instructions or cause unsafe actions. Overreliance describes a human
/organizational risk -trusting the model too much-rather than the model's intrinsic behavior of generating incorrect facts. Overreliance can be a consequence of this behavior, but it is not what the behavior itself is called.
In practice, you mitigate this kind of inaccuracy by grounding responses in trusted sources (for example, RAG), constraining prompts with explicit requirements, using verification steps (citations, cross-checking, tool-based validation), and adding human review for high-impact use cases.
質問 # 75
- Select the answer that correctly completes the sentence.
The cost of using generative AI language models is based typically on the number of __________ processed.
正解：
解説：
Explanation:
Most generative AI language model pricing is based on token consumption , which measures the amount of text processed by the model. Tokens are sub-word units used internally by language models (for example, parts of words, whole words, or punctuation). When you send a prompt, the model consumes input tokens (your prompt + any system instructions + retrieved grounding context). When it generates a response, it consumes output tokens (the generated completion). Costs typically scale with the total input + output tokens processed, which is why long prompts, large grounding passages, and lengthy responses increase spend. This also explains why prompt optimization, response length limits, caching, and careful grounding are common cost-control techniques in enterprise solutions.
By contrast, "documents" is too coarse (a document can be 1 page or 500 pages). "Requests" is not the primary unit for most LLM pricing models because request sizes vary dramatically. "Words" is not used because the model's actual compute unit is tokens, and tokenization differs across languages and text patterns.
Therefore, the most accurate completion is tokens .
質問 # 76
Your company uses a generative AI solution. You need to improve the quality of responses by using grounding. Which statement accurately describes how grounding improves accuracy and relevancy?
正解：A
解説：
Grounding is an AI solution pattern used to improve response quality by ensuring the model's output is based on trusted, relevant information provided at inference time, rather than relying only on what the model
"remembers" from training. Therefore, C is correct: grounding anchors responses in specific data sources .
In practical deployments, grounding commonly uses retrieval (often called Retrieval Augmented Generation, or RAG) where the system first finds relevant content from approved sources-such as internal policy documents, product documentation, knowledge bases, or databases-and then includes that content in the prompt context sent to the model. Microsoft's guidance describes grounding data as information supplied at inference time to help responses become more accurate and relevant because the model is guided by authoritative, up-to-date content that may not have been part of original training.
The other options do not define grounding. A relates to inclusion practices and diversity considerations, which are important for responsible AI but are not what grounding means. B describes transparency/explainability concepts. D relates to model evaluation/communication of limitations. Grounding is specifically about tying outputs to known sources, which reduces hallucinations and improves business trust in the generated responses.
質問 # 77
Your company creates a custom Azure Machine Learning model that uses a generative AI assistant. The model initially delivers strong results. However, six months later, the model predictions become noticeably less accurate. What is a possible cause of the issue?
正解：A
解説：
A common reason models degrade after being successful in production is data drift (also called concept drift).
Over time, the distribution of input data changes-for example, customer behavior shifts, product catalog changes, seasonality changes, new categories appear, sensors get recalibrated, or business processes evolve.
When the model sees data that differs from what it was trained on, its predictions can become less accurate.
This is exactly what option A describes and is the most likely "six months later" cause.
Option B is not a primary explanation for reduced predictive accuracy. More compute can improve throughput
/latency, but it does not inherently improve correctness of predictions. If anything, compute constraints typically cause timeouts or slower responses, not a systematic accuracy drop.
Option C (trained incorrectly) would usually manifest earlier-poor performance from the start-unless the
"incorrectness" is that the model was trained on a snapshot that later became stale (which again maps back to drift). The correct operational response is to monitor for drift, validate performance regularly, and retrain
/refresh the model using newer representative data and updated features/labels.
質問 # 78
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
Box: uses structured data and provides insights by using text, charts, tables, and other visuals.
The Analyst agent in Microsoft 365 Copilot _______________.
The Analyst agent in Microsoft 365 Copilot is a specialized, AI-powered "virtual data scientist" designed to transform raw, structured data into actionable insights, utilizing text, tables, charts, and graphs to present findings.
It is designed to work with structured data sources, including Excel files, CSVs, tables, and databases, to help users analyze data without needing advanced data science expertise.
Reference:
https://blog.storyals.com/sv/meet-your-new-ai-teammates-researcher-analyst
質問 # 79
......
当社MicrosoftのAB-731テストトレントを通じて、さらなる開発のための高効率の学習態度を構築するのに役立つこのような効率的な学習計画を設計する予定です。 AB-731学習教材は、あなたが学生やオフィスワーカー、グリーンハンド、または長年の経験を持つスタッフに関係なく、すべての候補者に対応します。Fast2testのAB-731認定トレーニングは絶対に良い選択です。 したがって、正確で有効なAB-731試験問題で成功することが保証されるため、AI Transformation Leader試験に合格できるかどうかを心配する必要はありません。
AB-731問題無料: https://jp.fast2test.com/AB-731-premium-file.html
ちなみに、Fast2test AB-731の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1zttGBUPl_aaQ8OwhJBDhIIqiCRz4HIOF