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| Section | Weight | Objectives |
|---|---|---|
| Artificial Intelligence workloads and considerations | 15–20% | - Describe responsible AI principles - Identify types of AI workloads - Describe considerations for developing AI solutions |
| Fundamental principles of machine learning on Azure | 15–20% | - Describe automated machine learning - Describe core concepts of machine learning - Describe machine learning pipelines - Describe capabilities of Azure Machine Learning |
| Features of generative AI workloads on Azure | 20–25% | - Describe use cases for generative AI - Describe responsible AI practices for generative AI - Describe generative AI concepts - Describe capabilities of Azure OpenAI Service |
| Features of Natural Language Processing (NLP) workloads on Azure | 15–20% | - Describe capabilities of Azure Language - Identify types of NLP solutions - Describe capabilities of Azure Translator - Describe capabilities of Azure Speech |
| Features of computer vision workloads on Azure | 15–20% | - Identify types of computer vision solutions - Describe capabilities of Azure Face - Describe capabilities of Azure Custom Vision - Describe capabilities of Azure Form Recognizer - Describe capabilities of Azure Computer Vision |
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NEW QUESTION # 295
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
Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
NEW QUESTION # 296
You need to develop a chatbot for a website. The chatbot must answer users' questions based on the information in the following documents:
A product troubleshooting guide in a Microsoft Word document
A frequently asked questions (FAQ) list on a webpage
Which service should you use to process the documents?
Answer: B
NEW QUESTION # 297
To complete the sentence, select the appropriate option in the answer area.
Answer:
Explanation:
Explanation:
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Incorrect Answers:
Not features: In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Reference:
https://www.cloudfactory.com/data-labeling-guide
NEW QUESTION # 298
Select the answer that correctly completes the sentence.
Answer:
Explanation:
NEW QUESTION # 299
Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot.
Which type of AI workload should you use?
Answer: A
Explanation:
Section: Describe features of Natural Language Processing (NLP) workloads on Azure Explanation:
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language- processing
NEW QUESTION # 300
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