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The AI-900 certification exam is primarily designed for business stakeholders and technical professionals who want to gain a fundamental understanding of AI technologies and their potential applications in Microsoft Azure services. Microsoft Azure AI Fundamentals certification is an entry-level exam that validates the candidate's knowledge of AI concepts and Azure AI services, including Cognitive Services, Machine Learning, and Bot Services. AI-900 exam also covers the ethical and responsible use of AI, including privacy, security, and compliance considerations.
Overall, AI-900 exam tests the applicant's understanding of AI and ML through various topics categorized into 5 domains:
The first topic of the final exam deals with AI workloads that are usually common. It includes candidates' understating various types of workloads such as predicting forecasting traits, the detection of anomalies, vision for computer systems, NLP, and conversational AI workloads. The examinees are also required to have a thorough understanding of the principles of AI that possess responsibility traits. It means that the candidate needs to be well aware of all the considerations for different solutions for AI which include fairness, reliability, security, privacy, inclusiveness, transparency facets, and finally, functions for accountability.
In the second section, the applicant's knowledge of fundamental principles of machine learning in relation to Azure cloud computing services will be tested. This requires the applicant to have a clear comprehension of all the common types of various scenarios for ML that include classification, regression, and clustering. It is also essential to have a thorough understanding of the core MI concepts such as labels & features in datasets, dataset training that go with the validation process, etc. The entrant must as well as be capable of identifying the core task required in developing an ML solution. So, understanding the ingestion and preparation of data chunks, selection and engineering of various features, evaluation procedure as well as training for models, and deployment & management of those models will be vital. Finally, this portion of AI-900 Exam includes concepts of ML automated user interface that are ML Wizard UI and Azure ML designer.
This segment of the official test is all about workloads for computer vision in relation to Azure services. To get through this part, one must have a general grasp of the various common forms of computer vision solutions that include image classification, object detection, semantic segmentation, recognition for the optical character, and facial detection. The applicants are also required to have an understanding of the diverse methods including services used in Azure for activities that utilize computer vision. Such methods and services include the vision for computers, vision for custom, services for face vision, and at last, the recognizer of the form.
The fifth and final domain for such an exam tests the entrant's understanding of conversational AI workloads. This requires the comprehension of facets as well as cases for the usage of conversational traits for AI which includes web chatbots, voice menus for the telephone, and digital assistants used for personal purposes. The applicant also needs to know how to check various Azure services related to conversational AI. Additionally, they should have a firm understanding of the QnA Maker Service & Bot service. Finally, it is essential for candidates to know and be able to describe the common characteristics of conversational artificial intelligence solutions.
The fourth portion of AI-900 exam deals with Natural Language Processing or NLP workloads in Azure. To get a passing score, the entrant must have a firm grasp on the facets and usage of diverse NLP scenarios for workloads which comprise the extraction of the key phrase, the recognition of entities, the analysis of sentiments, modeling for languages, the recognition of speech, and translation traits. The applicants are also required to know about different techniques as well as services of Azure used for various NLP workloads. They are required to be capable of identifying diverse abilities of various services such as text analysis, LUIS, speech, and translator text.
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The AI-900 exam covers topics such as machine learning, natural language processing, computer vision, and conversational AI. AI-900 exam also covers the different AI services and tools that are available on the Microsoft Azure platform. These include Azure Machine Learning, Azure Cognitive Services, and Bot Services. Individuals who pass the AI-900 Exam will have a good understanding of how these AI services can be used to solve various business problems.
NEW QUESTION # 29
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:
The Azure Text Analytics service, a component of Azure Cognitive Services, provides natural language processing (NLP) capabilities to analyze and understand text-based data. According to the Microsoft Azure AI Fundamentals (AI-900) official study guide and Microsoft Learn module "Identify features and uses for Natural Language Processing (NLP)", the Text Analytics service supports multiple text understanding tasks, such as language detection, key phrase extraction, sentiment analysis, and entity recognition.
* Language Identification - Yes:Text Analytics can automatically detect the language in which text is written. This feature analyzes linguistic patterns and assigns a language code (for example, "en" for English, "es" for Spanish). It is one of the primary features described in Microsoft Learn as part of the service's Language Detection API.
* Detect Handwritten Signatures - No:Detecting handwritten signatures is not a text-based NLP task.
Instead, it belongs to the computer vision domain, specifically Optical Character Recognition (OCR).
The Text Analytics service only processes digital text, not handwritten or image-based data. To detect handwriting or signatures, you would use the Computer Vision OCR API, not Text Analytics.
* Entity Recognition - Yes:The Text Analytics service can identify named entities-such as people, locations, organizations, dates, and quantities-within documents. This is known as Named Entity Recognition (NER), which helps extract structured information from unstructured text.
NEW QUESTION # 30
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
NEW QUESTION # 31
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
NEW QUESTION # 32
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://machinelearningmastery.com/difference-test-validation-datasets/
NEW QUESTION # 33
When training a model, why should you randomly split the rows into separate subsets?
Answer: B
Explanation:
Section: Describe fundamental principles of machine learning on Azure
NEW QUESTION # 34
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