AI-900 Online Practice Questions and Answers

Correct Answer:

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

Correct Answer:

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results

Correct Answer:

Regression is a machine learning task that is used to predict the value of the label from a set of related features.

Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks

Correct Answer:

Correct Answer:

Correct Answer:

Box 1: Speech

Custom Speech: Code-free automated machine learning for speech recognition

Speech to text is a Speech service feature that accurately transcribes spoken audio to text.

Make spoken audio actionable

Quickly and accurately transcribe audio to text in more than 100 languages and variants. Customize models to enhance accuracy for domain-specific terminology. Get more value from spoken audio by enabling search or analytics on

transcribed text or facilitating action—all in your preferred programming language.

Box 2: Language service

Add intents to your LUIS app to identify groups of questions or commands that have the same intention.

Note: Language understanding (LU) is a very centric component to enable conversational services such as bots, IoT experiences, analytics, and others. In a spoken dialog system, LU converts from the words in a sentence into a machine-

readable meaning representation, typically indicating the intent of the sentence and any present entities. For example, consider a physical fitness domain, with a dialog system embedded in a wearable device like a watch. This dialog system

could recognize intents like StartActivity and StopActivity, and could recognize entities like ActivityType. In the user input “begin a jog”, the goal of LU is to identify the intent as StartActivity, and identify the entity ActivityType= ’’jog’’.

Box 3: Language service

Intent compared to entity

The intent represents the action the application should take for the user, based on the entire utterance. An utterance can have only one top-scoring intent, but it can have many entities.

Create an intent when the user's intention would trigger an action in your client application, like a call to the checkweather() function from the table above. Then create entities to represent parameters required to execute the action.

Reference: https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text

https://azure.microsoft.com/en-us/blog/luis-ai-automated-machine-learning-for-custom-language-understanding/

https://docs.microsoft.com/en-us/azure/cognitive-services/luis/concepts/intents

Correct Answer: A

Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.

Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/

Correct Answer: B

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

Correct Answer: ACE

Automatic extraction

Extract question-answer pairs from semi-structured content, including FAQ pages, support websites, excel files, SharePoint documents, product manuals and policies.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/concepts/content-types

Correct Answer: B

QnA Maker is a cloud-based API service that lets you create a conversational question-and-answer layer over your existing data. Use it to build a knowledge base by extracting questions and answers from your semi-structured content, including FAQs, manuals, and documents. Answer users' questions with the best answers from the QnAs in your knowledge base--automatically. Your knowledge base gets smarter, too, as it continually learns from user behavior. Incorrect Answers:

A: 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.

D: Azure Cognitive Services Face Detection API: At a minimum, each detected face corresponds to a faceRectangle field in the response. This set of pixel coordinates for the left, top, width, and height mark the located face. Using these coordinates, you can get the location of the face and its size. In the API response, faces are listed in size order from largest to smallest.

Reference: https://azure.microsoft.com/en-us/services/cognitive-services/qna-maker/

Correct Answer: AC

A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.

C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled. Incorrect Answers:

B: F1 score also known as balanced F-score or F-measure is used to evaluate a classification model.

D: aucROC or area under the curve (AUC) is used to evaluate a classification model.

Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics

Correct Answer: A

Key phrase extraction is the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s). Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can perform advanced natural language processing over raw text.

https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing

Correct Answer: B

Utterances are input from the user that your app needs to interpret.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-utterance

Correct Answer: C

Regression is a supervised machine learning technique used to predict numeric values.

Reference: https://learn.microsoft.com/en-us/training/modules/create-regression-model-azure-machine-learning-designer/

Correct Answer: D

The Cognitive Services Speech SDK provides two ways to recognize intents. An intent is something the user wants to do: book a flight, check the weather, or make a call. Using intent recognition, your applications, tools, and devices can determine what the user wants to initiate or do based on options you define in the Intent Recognizer or Conversational Language Understanding (CLU) model.

1.

Pattern matching

The Speech SDK provides an embedded pattern matcher that you can use to recognize intents in a very strict way. This is useful for when you need a quick offline solution. This works especially well when the user is going to be trained in

some way or can be expected to use specific phrases to trigger intents.

2.

Conversational Language Understanding

Conversational language understanding (CLU) enables users to build custom natural language understanding models to predict the overall intention of an incoming utterance and extract important information from it.

Both a Speech resource and Language resource are required to use CLU with the Speech SDK. The Speech resource is used to transcribe the user's speech into text, and the Language resource is used to recognize the intent of the utterance.

Reference: https://learn.microsoft.com/en-us/azure/cognitive-services/speech-service/intent-recognition