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Microsoft Azure AI Fundamentals Sample Questions (Q48-Q53):

NEW QUESTION # 48
You plan to deploy an Azure Machine Learning model by using the Machine Learning designer Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Identify features of common machine learning types", the standard workflow for creating and deploying a machine learning model - especially within Azure Machine Learning Designer - follows a structured sequence of steps to ensure that the model is trained effectively and evaluated correctly.
Here's the detailed breakdown of the correct order:
* Import and prepare a dataset:This is always the first step in the machine learning lifecycle. The dataset is imported into Azure Machine Learning and cleaned or preprocessed. Preparation might include handling missing values, normalizing data, removing outliers, and encoding categorical variables. This ensures the dataset is ready for modeling.
* Split the data randomly into training data and validation data:The dataset is then divided into two parts
- the training set and the validation (or testing) set. Typically, around 70-80% of the data is used for training and 20-30% for validation. This step ensures that the model can be evaluated on unseen data later, preventing overfitting.
* Train the model:During this stage, the machine learning algorithm learns patterns from the training data. Azure Machine Learning Designer provides multiple algorithms (classification, regression, clustering, etc.) that can be applied using "Train Model" components.
* Evaluate the model against the validation dataset:Finally, the trained model's performance is tested using the validation dataset. Evaluation metrics such as accuracy, precision, recall, or RMSE (depending on the model type) are calculated to assess how well the model generalizes to new data.
The incorrect option - "Evaluate the model against the original dataset" - is not used in proper ML workflows, because evaluating on the same data used for training would give misleadingly high accuracy due to overfitting.


NEW QUESTION # 49
To complete the sentence, select the appropriate option in the answer area.

Answer:

Explanation:
Explanation
Text Description automatically generated

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-label-data


NEW QUESTION # 50
For each of the following statements, select Yes if the statement is True. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
< Smart home device answering questions # Yes
Webchat using Azure Bot Service # Yes
Auto-caption generation for videos # No
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and the Microsoft Learn module "Describe features of common AI workloads", Conversational AI refers to systems designed to engage in human-like dialogue through natural language-either text or speech. These systems include chatbots, virtual assistants, and voice-enabled smart devices, all of which can interpret user intent and respond meaningfully.
* A smart device in the home that responds to questions such as "When is my next appointment?" - Yes.
This is a classic example of Conversational AI. Devices like smart speakers or personal digital assistants use speech recognition, natural language understanding (NLU), and language generation to interpret spoken input and respond conversationally. The AI-900 study materials identify these as examples of voice-based conversational AI systems, which fall under the Speech and Language AI workloads.
* An interactive webchat feature on a company website can be implemented by using Azure Bot Service
- Yes.This statement is true. The Azure Bot Service is the primary Microsoft tool for creating and deploying conversational bots across various channels, including websites, Microsoft Teams, and other messaging platforms. The AI-900 syllabus specifically cites this as an example of implementing conversational AI for customer support or information retrieval.
* Automatically generating captions for pre-recorded videos is an example of conversational AI - No.
This is not conversational AI; instead, it falls under the Speech AI workload, specifically speech-to-text transcription. Automatically generating captions involves converting audio from video into written text but does not involve dialogue or interaction between a user and a system.
Thus, based on the official AI-900 guidance, only the first two scenarios describe conversational AI use cases.


NEW QUESTION # 51
Match the services to the appropriate descriptions.
To answer, drag the appropriate service from the column on the left to its description on the right. Each service may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point

Answer:

Explanation:

Explanation:


NEW QUESTION # 52
You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of Al workload should you use?

Answer: A

Explanation:
According to the AI-900 study guide and Microsoft Learn module "Identify features of natural language processing workloads", Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. Tasks such as extracting key terms, summarizing documents, identifying topics, or determining sentiment fall under NLP workloads.
In this question, you have insurance claim reports stored as text, and you need to extract key terms to generate summaries. This matches the Text Analytics service in Azure Cognitive Services, which uses NLP techniques such as key phrase extraction to identify important concepts within textual data.
The other options are incorrect because:
* A. Conversational AI focuses on chatbots or dialogue systems.
* B. Anomaly detection identifies unusual data patterns, not textual meaning.
* D. Computer vision processes image or video content, not text.
Therefore, extracting key terms from documents is a clear example of Natural Language Processing.
Reference:Microsoft Learn - Identify features of Natural Language Processing (NLP) workloads


NEW QUESTION # 53
......

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