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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
|
| Topic 2: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
|
| Topic 3: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
|
| Topic 4: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
|
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NEW QUESTION # 15
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:
This question assesses knowledge of the Azure Cognitive Services Speech and Text Analytics capabilities, as described in the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn modules "Explore natural language processing" and "Explore speech capabilities." These services are part of Azure Cognitive Services, which provide prebuilt AI capabilities for speech, language, and text understanding.
* You can use the Speech service to transcribe a call to text # YesThe Speech-to-Text feature in the Azure Speech service automatically converts spoken words into written text. Microsoft Learn explains:
"The Speech-to-Text capability enables applications to transcribe spoken audio to text in real time or from recorded files." This makes it ideal for call transcription, voice assistants, and meeting captioning.
* You can use the Text Analytics service to extract key entities from a call transcript # YesOnce a call has been transcribed into text, the Text Analytics service (part of Azure Cognitive Services for Language) can process that text to extract key entities, key phrases, and sentiment. For example, it can identify names, organizations, locations, and product mentions. Microsoft Learn notes: "Text Analytics can extract key phrases and named entities from text to derive insights and structure from unstructured data."
* You can use the Speech service to translate the audio of a call to a different language # YesThe Azure Speech service also includes Speech Translation, which can translate spoken language in real time. It converts audio input from one language into translated text or speech output in another language.
Microsoft Learn describes this as: "Speech Translation combines speech recognition and translation to translate spoken audio to another language."
NEW QUESTION # 16
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:
NEW QUESTION # 17
You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?
Answer: C
Explanation:
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m
NEW QUESTION # 18
Select the answer that correctly completes the sentence.
Answer:
Explanation:
Explanation:
"features."
According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Describe fundamental principles of machine learning on Azure," in a machine learning model, the data used as inputs are known as features, while the data that represents the output or target prediction is known as the label.
Features are measurable attributes or properties of the data used by a model to learn patterns and make predictions. They are also referred to as independent variables because they influence the result that the model tries to predict. For example, in a machine learning model that predicts house prices:
* Features might include square footage, location, and number of bedrooms, while
* The label would be the house price (the value being predicted).
In the context of Azure Machine Learning, during model training, features are passed into the algorithm as input variables (X-values), and the label is the corresponding output (Y-value). The model then learns the relationship between the features and the label.
Let's review the incorrect options:
* Functions: These are mathematical operations or relationships used inside algorithms, not the input data itself.
* Labels: These are the outputs or results that the model predicts, not the inputs.
* Instances: These refer to individual data records or rows in the dataset, not the input fields themselves.
Hence, in any supervised or unsupervised learning process, the input data (independent variables) are called features, and the model uses them to predict labels (dependent variables).
NEW QUESTION # 19
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 # 20
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