Microsoft AI-900 Actual Free Exam Questions & Community Discussion
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
# Yes - Extract key phrases
# No - Generate press releases
# Yes - Detect sentiment
The Azure AI Language service is a powerful set of natural language processing (NLP) tools within Azure Cognitive Services, designed to analyze, understand, and interpret human language in text form. According to the Microsoft Azure AI Fundamentals (AI-900) study guide and Microsoft Learn documentation, this service includes several capabilities such as key phrase extraction, sentiment analysis, language detection, named entity recognition (NER), and question answering.
* Extract key phrases from documents # YesThe Key Phrase Extraction feature identifies the most relevant words or short phrases within a document, helping summarize important topics. This is useful for indexing, summarizing, or organizing content. For instance, from "Azure AI Language helps analyze customer feedback," it may extract "Azure AI Language" and "customer feedback" as key phrases.
* Generate press releases based on user prompts # NoThis functionality falls under generative AI, specifically within Azure OpenAI Service, which uses models such as GPT-4 for text creation. The Azure AI Language service focuses on analyzing and understanding existing text, not generating new content like press releases or articles.
* Build a social media feed analyzer to detect sentiment # YesThe Sentiment Analysis capability determines the emotional tone (positive, neutral, negative, or mixed) of text data, making it ideal for analyzing social media posts, reviews, or feedback. Businesses often use this to gauge customer satisfaction or brand reputation.
In summary, the Azure AI Language service analyzes text to extract insights and detect sentiment but does not generate new textual content.
Match the Al workload to the appropriate task.
To answer, drag the appropriate Al workload from the column on the left to its task on the right. Each workload may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
To answer, drag the appropriate Al workload from the column on the left to its task on the right. Each workload may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
Correct Answer:
Match the tool to the Azure Machine Learning task.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
To answer, drag the appropriate tool from the column on the left to its tasks on the right. Each tool may be used once, more than once, or not at all NOTE: Each correct match is worth one point.
Correct Answer:
Explanation:
The correct matching aligns directly with the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules under "Identify features of Azure Machine Learning". Azure Machine Learning provides a suite of tools that serve different functions within the model development lifecycle - from creating workspaces, to training models, to automating experimentation.
* The Azure portal # Create a Machine Learning workspace.The Azure portal is a web-based graphical interface for managing all Azure resources. According to Microsoft Learn, you use the portal to create and configure the Azure Machine Learning workspace, which acts as the central environment where datasets, experiments, models, and compute resources are organized. Creating a workspace through the portal involves specifying a subscription, resource group, and region - tasks that are part of the setup stage rather than model development.
* Machine Learning designer # Use a drag-and-drop interface used to train and deploy models.The Machine Learning designer (formerly "Azure ML Studio (classic)") provides a visual, no-code/low- code interface for building, training, and deploying machine learning pipelines. The designer uses a drag-and-drop workflow where users connect modules representing data transformations, model training, and evaluation. This tool is ideal for beginners and those who want to quickly experiment with machine learning concepts without writing code.
* Automated machine learning (Automated ML) # Use a wizard to select configurations for a machine learning run.Automated ML simplifies model creation by automatically selecting algorithms, hyperparameters, and data preprocessing options. Users interact through a guided wizard (within the Azure Machine Learning studio) that walks them through configuration steps such as selecting datasets, target columns, and performance metrics. The system then iteratively trains and evaluates multiple models to recommend the best-performing one.
Together, these tools streamline the machine learning workflow:
* Azure portal for setup and resource management,
* Machine Learning designer for visual model creation, and
* Automated ML for guided, automated model selection and tuning.
You need to implement a pre-built solution that will identify well-known brands in digital photographs.
Which Azure Al sen/tee should you use?
Which Azure Al sen/tee should you use?
Correct Answer: C
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You are building an Al-based loan approval app.
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible Al principle?
You need to ensure that the app documents why a loan is approved or rejected and makes the report available to the applicant.
This is an example of which Microsoft responsible Al principle?
Correct Answer: A
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For a machine learning progress, how should you split data for training and evaluation?
Correct Answer: C
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Which three actions improve the quality of responses returned by a generative Al solution that uses GPT-3.5?
Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Correct Answer: B,C,E
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To complete the sentence, select the appropriate option in the answer area.
Correct Answer:
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud.
Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference:
https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
Select the answer that correctly completes the sentence.
Correct Answer:
Explanation:
According to the Microsoft Azure AI Fundamentals (AI-900) Official Study Guide and the Microsoft Learn module "Explore computer vision in Microsoft Azure," computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world - such as images or videos.
In this scenario, the task is to count the number of animals in an area based on a video feed. This requires the system to:
* Detect the presence of animals in each frame of the video (object detection).
* Track and count them across multiple frames as they move.
These are classic computer vision tasks, as they involve analyzing visual inputs (video or image data) and identifying objects (in this case, animals). Azure provides services such as Azure Computer Vision, Custom Vision, and Video Indexer, which can perform object detection, counting, and activity recognition using AI models trained on visual datasets.
Why the other options are incorrect:
* Forecasting: Involves predicting future values based on historical data (e.g., predicting sales or weather), not analyzing video feeds.
* Knowledge mining: Focuses on extracting insights from large text-based document repositories, not images or videos.
* Anomaly detection: Identifies unusual patterns in numeric or time-series data, not visual objects.
Therefore, identifying and counting animals in video footage falls under computer vision, since it uses AI to visually detect, classify, and quantify objects in real-time or recorded feeds.
In which scenario should you use key phrase extraction?
Correct Answer: A
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