HOTSPOT (Drag and Drop is not supported)Select the answer that correctly completes the sentence.Hot Area:
Answer(s): A
Box: Information extraction solutions that detect and read text in scanned documents and images rely on ______________.Information extraction relies primarily on Optical Character Recognition (OCR), which is a specialized branch of computer vision. Instead of simply looking at pixels, these tools use advanced AI and machine learning models to "see" and interpret visual patterns.
https://www.ultralytics.com/blog/popular-open-source-ocr-models-and-how-they-work
You have a Microsoft Foundry project that has a generative AI model deployment.You need to ensure that responses generated by the model minimize costs and remain within a defined length.Which parameter should you configure?
Answer(s): C
The Max Completion Tokens (or max_tokens / max_output_tokens in the API) parameter is exactly the tool you use to enforce a fixed length on AI-generated responses and keep costs predictable.How the Parameter Works What it does: It sets a strict upper bound on the number of output tokens the model can generate for your completion. Once this number is reached, the model will stop generating (cut off) regardless of whether it finished its thought.What it doesn't do: It does not set a minimum length or force the model to write exactly that amount. It only acts as a ceiling.
https://learn.microsoft.com/en-us/answers/questions/5828812/reasoning-models-like-5-1-fails-with-500-error-cod
HOTSPOT (Drag and Drop is not supported)For each of the following statements, select Yes if the statement is true. Otherwise, select No.Note: Each correct selection is worth one point.Hot Area:
Box 1: Yes Yes - Human-in-the-loop practices provide accountability for AI-generated decisions.Human-in-the-loop (HITL) practices are foundational for ensuring AI accountability in Azure. Rather than granting models total autonomy, HITL establishes control layers, pause-and-approval mechanisms, and audit trails so that human operators remain the final authority on critical decisions.Box 2: No No - Deploying an AI system to production environment eliminites the need for ongoing monitoring.Deploying an AI system to a production environment does not eliminate the need for ongoing monitoring. AI applications face unique challenges like real-world data shifts, unpredictable user interactions, and changing environments, making continuous post-deployment observation essential to maintaining reliability, trust, and safety.Box 3: Yes Yes - disclosing the team that designed and deployed an AI system provides accountability for the system’s output.Under Microsoft's Responsible AI framework for Azure, disclosing the people and teams who design and deploy an AI system is a core mechanism for ensuring accountability.Microsoft explicitly states that accountability means the people who design and deploy AI systems must be responsible for how those systems operate. Clearly identifying these teams establishes human oversight and prevents the AI from being treated as the final authority on decisions.
https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai https://verifywise.ai/lexicon/post-deployment-monitoring
Your company processes customer support emails.You need to implement an AI solution that automatically identifies mentions of people, organizations, and locations in the emails.Which text analysis technique should you use?
The adequate text analysis feature is Named Entity Recognition (NER), which is a core prebuilt capability of the Azure AI Language service.Prebuilt Categorization: The prebuilt NER feature automatically parses unstructured text (like emails) to identify and group elements into standard classes, including Person, Organization, and Location.No Training Required: Because it relies on state-of-the-art pretrained transformer models, it can be deployed immediately out-of-the-box without requiring data labeling or custom model training. High Extensibility: If your emails contain highly specialized terms (such as proprietary product IDs or industry-specific roles), you can easily scale to Custom NER within the same architecture.
https://learn.microsoft.com/en-us/azure/ai-services/language-service/named-entity-recognition/overview
Box: accountability Ensuring that human reviewers oversee AI-generated decisions and remain responsible for the final outout is an example if the Microsoft responsible AI princioke of ___________.The principle of accountability states that the people who design, deploy, and use AI systems must remain responsible and answerable for how those systems operate and the outcomes they produce. Under Microsoft's Responsible AI Principles, accountability is defined by the following key ideas: Human Oversight: AI should not be the sole decision-maker for critical matters; human reviewers must have the ability to supervise, control, and override AI-generated outputs. Ultimate Responsibility: Even when AI automates tasks or processes, humans and organizations remain legally and ethically responsible for the final decision.
https://thedatacommunity.org/2026/05/18/describe-considerations-for-accountability-in-an-ai-solution-ai-901-exam-prep/
You need to build an AI solution that generates marketing email drafts based on a short description of a product and its target audience.Which AI workload should you use?
A Generative AI workload is exactly what should be used for this scenario. Generative AI models (such as LLMs) are specifically designed to interpret prompts and create original, context-aware content, making them the perfect fit for drafting tailored marketing emails based on a product description and target audience.
https://azure.microsoft.com/en-us/products/ai-foundry/tools/content-understanding
Box 1: Yes Yes - The temperature parameter can be set before deploying a model.The temperature parameter can be set before deploying a model. Its actual enforcement depends on where and how the model is being deployed.Box 2: No No - During inference, the model name us used to route requests to a specific deployment.During inference, the deployment name is used to route requests to a specific deployment, not the model name.When you deploy a foundational model in Azure AI (such as Azure OpenAI or Azure AI Foundry models), you assign it a custom unique deployment name. During inference, you must pass this deployment name into the model parameter of your SDK call or request body.Box 3: Yes Yes - After a model is deployed, the model deployed, both code and testing tools can be used to interact with the model?After you deploy a model in Azure AI (such as in Azure AI Foundry or Azure Machine Learning), you can interact with it using both custom code and native testing tools.
https://github.com/shivamag125/EM_PT https://learn.microsoft.com/en-us/azure/foundry/foundry-models/concepts/endpoints
Box: fairness Evaluating model outcomes across demographic groups to reduce bias is an example of the Microsoft responsible AI principle of ________.Evaluating model outcomes across demographic groups to reduce bias falls squarely under Microsoft's responsible AI principle of Fairness.According to Microsoft's AI framework, AI systems should treat all people fairly, which requires teams to analyze how a system's predictions and recommendations impact different groups of people and actively mitigate issues like stereotyping, bias, and unfairness.
Share your comments for Microsoft AI-901 exam with other users:
please upload 1z0-1072-23 exam dups
i was hoping if you could please share the pdf as i’m currently preparing to give the exam.
i am looking for oracle 1z0-116 exam
where we can get the answer to the questions
nice questions
question 129 is completely wrong.
i need dump
love the site.
can you please upload it back?
could you please re-upload this exam? thanks a lot!
great about shared quiz
goood helping
pay attention to questions. they are very tricky. i waould say about 80 to 85% of the questions are in this exam dump.
wish you would allow more free questions
great simulation
very g inood
q35 should be a
sap c_ts450_2021
ecellent materil for unserstanding
good so far
this is way too informative
very helpfull
q.189 - answers are incorrect.
awesome job in getting these questions
i cant find aws certified practitioner clf-c01 exam in aws website but i found aws certified practitioner clf-c02 exam. can everyone please verify the difference between the two clf-c01 and clf-c02? thank you
grazie mille. i got a satisfactory mark in my exam test today because of this exam dumps. sorry for my english.
some of the answers are incorrect. need to be reviewed.
so far so good
i am really liking it
thanks good stuff
need dump c_tadm_23
next time i will write a full review
first time using this site
Keeping this site free takes real effort. We constantly battle automated scraping and unauthorized content copying. A quick account helps us protect the community and keep the site free.
To continue studying for your AI-901, please sign in or create a free account.