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| Section | Weight | Objectives |
|---|
| Topic 1: Describe AI workloads and considerations | 20-25% | - Fundamentals of artificial intelligence concepts
- 1. Responsible AI principles
- 2. Common AI workloads
|
| Topic 2: Features of computer vision workloads on Azure | 15-20% | - Computer vision solutions
- 1. Image classification
- 2. OCR and image analysis
- 3. Object detection
|
| Topic 3: Fundamentals of machine learning on Azure | 25-30% | - Core machine learning concepts
- 1. Supervised vs unsupervised learning
- 2. Training and validation concepts
|
| Topic 4: Features of natural language processing (NLP) workloads on Azure | 30-35% | - Text analytics and language understanding
- 1. Key phrase extraction
- 2. Language modeling and translation
- 3. Sentiment analysis
|
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Hot Trustworthy AI-900 Dumps Free PDF | Latest Test AI-900 King: Microsoft Azure AI Fundamentals
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Microsoft Azure AI Fundamentals Sample Questions (Q275-Q280):
NEW QUESTION # 275
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

NEW QUESTION # 276
Match the Azure Cognitive Services to the appropriate Al workloads.
To answer, drag the appropriate service from the column on the left to its workload 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:

NEW QUESTION # 277
To complete the sentence, select the appropriate option in the answer area.

Answer:
Explanation:

Explanation

Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/home custom vision - This is a type of computer vision service which helps in building/training models using user provided data Creating an object detection solution with Custom Vision consists of three main tasks. First you must use upload and tag images, then you can train the model, and finally you must publish the model so that client applications can use it to generate predictions.
https://docs.microsoft.com/en-us/learn/modules/detect-objects-images-custom-vision/2-object-detection-azure
NEW QUESTION # 278
Select the answer that correctly completes the sentence.

Answer:
Explanation:

Explanation:

In Azure Machine Learning Designer, the Dataset output visualization feature is specifically used to explore and understand the distribution of values in potential feature columns before model training begins. This capability is critical for data exploration and preprocessing, two essential stages of the machine learning pipeline described in the Microsoft Azure AI Fundamentals (AI-900) and Azure Machine Learning learning paths.
When a dataset is imported into Azure Machine Learning Designer, users can right-click on the dataset output port and select "Visualize". This launches the dataset visualization pane, which provides detailed statistical summaries for each column, including:
* Data type (numeric, categorical, string, Boolean)
* Minimum, maximum, mean, and standard deviation values for numeric columns
* Frequency counts and distinct values for categorical columns
* Missing value counts
This visual inspection helps determine which columns should be used as features, which might need normalization or encoding, and which contain missing or irrelevant data. It is a vital step in ensuring the dataset is clean and ready for model training.
Let's examine why other options are incorrect:
* Normalize Data module is used to scale numeric data, not to visualize distributions.
* Select Columns in Dataset module is used to include or exclude columns, not to analyze them.
* Evaluation results visualization feature is used after model training to interpret performance metrics like accuracy or recall, not data distributions.
Therefore, based on official Microsoft documentation and AI-900 study materials, to explore the distribution of values in potential feature columns, you use the Dataset output visualization feature in Azure Machine Learning Designer.
NEW QUESTION # 279
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:

Reference:
https://azure.microsoft.com/en-us/services/machine-learning/automatedml/#features
NEW QUESTION # 280
......
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