The Azure Machine Learning designer is a low-code/no-code tool within the Azure ML workspace. Its primary interface is a visual canvas where users build end-to-end ML pipelines by dragging, dropping, and connecting pre-built modules for tasks like data import, transformation, model training, and scoring.

• Option 3 (automatically selecting an algorithm...) is incorrect as this specifically describes Automated ML (AutoML), a separate feature.
• Option 4 (using a code-first notebook experience) is incorrect as this describes using Jupyter notebooks with the Azure ML SDK, which is the code-based alternative to the visual designer.

Microsoft. (2024, May 22). What is Azure Machine Learning designer? Microsoft Learn.

Section: Introduction, Paragraphs 1-2.

Quote: "Azure Machine Learning designer is a drag-and-drop interface... The designer provides a visual canvas where you can add datasets and modules... You connect the modules to create a pipeline draft..."

URL: https://learn.microsoft.com/en-us/azure/machine-learning/concept-designer

Microsoft. (2024, October 17). What is automated machine learning (AutoML)? Microsoft Learn.

Section: "How AutoML works."

Note: This official documentation confirms that "automatically selecting an algorithm" is the function of AutoML, distinguishing it from the designer.

URL: https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

Duke University. (n.d.). AI for Everyone: Azure ML Designer. Duke AIPI (Artificial Intelligence for Product Innovation).

Section: "Azure ML Designer."

Quote: "Azure ML Designer... is a visual, drag-and-drop environment... In the Designer, you create an ML 'pipeline' on a visual canvas by dragging and dropping 'modules'..."

URL: https://aipi.pratt.duke.edu/azure-ml-designer

Microsoft. (2024, September 24). What are Jupyter notebooks in Azure Machine Learning? Microsoft Learn.

Section: Introduction.

Note: This documentation defines the "code-first notebook experience" as a separate component (Jupyter notebooks) within the Azure ML workspace, confirming it is distinct from the designer.

URL: https://learn.microsoft.com/en-us/azure/machine-learning/concept-notebooks

The sequence represents the standard, fundamental workflow for creating a machine learning model before it can be deployed.

1. Data Preparation: Raw data must first be collected, cleaned, transformed, and structured (e.g., feature engineering, splitting into training and test sets). A model cannot be trained on unprepared data.
2. Model Training: The prepared training dataset is used to "teach" an algorithm, allowing it to learn patterns and create the model.
3. Model Evaluation: After training, the model's performance must be validated using a separate test dataset (which it has not seen before) to measure its accuracy and effectiveness. A model is only deployed if its evaluation results meet the required business objectives.
4. The other options, model retraining and data encryption, are incorrect for this sequence. Model retraining is a post-deployment process performed to update a model when its performance degrades over time (model drift). Data encryption is a security process applied to data at rest or in transit, not a sequential step in the model creation pipeline itself.

Source: Microsoft (n.d.). What is Azure Machine Learning designer? Microsoft Docs.

Reference: In the section titled "Build a pipeline," the documentation describes the visual workflow: "Pipelines start with your data... you can connect these datasets to other modules which prepare data, train models, and evaluate models." This officially confirms the sequential order of these three core processes.

Source: Amershi, S., Begel, A., Bird, C., et al. (2019). Software Engineering for Machine Learning: A Case Study. 2019 IEEE/ACM 41st International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP).

Reference: Page 292 (Section III.A, "AI Development Workflow"). The paper describes the typical ML workflow stages identified through case studies, stating: "...the AI development workflow includes data preparation (e.g., collect, clean, label), model training (e.g., feature engineering, training), and model evaluation..."

DOI: https://doi.org/10.1109/ICSE-SEIP.2019.00018

Source: Microsoft (n.d.). The machine learning lifecycle. Microsoft Docs.

Reference: The document explicitly outlines the stages of the machine learning lifecycle. The core model creation process is described in this order: 3. Data preparation, 4. Model training, and 5. Model evaluation, which all occur before stage 6. Model deployment. This clearly separates the pre-deployment steps from post-deployment activities like "Model monitoring" (which leads to retraining).

In Azure Machine Learning, registering a model is the standard process for storing and versioning a trained model within your workspace's model registry. When you register a model with a specific name, it is saved as version 1. If you subsequently train a new version of the model and register it using the same name, Azure Machine Learning automatically increments the version number (e.g., to version 2). This functionality is central to MLOps practices, as it allows you to track, organize, and manage the lifecycle of all your trained models, ensuring reproducibility and enabling you to select a specific version for deployment.

A. Provision an inference duster.

An inference cluster is a compute target used for deploying a model to perform real-time predictions, not for tracking or versioning the model asset itself.

B. Explain the model.

Explaining a model is part of Responsible AI and involves generating interpretations for its predictions. This process does not track or version the model.

D. Register the training data.

Registering data creates a versioned dataset in the workspace. While this tracks the data used for training, it does not track the resulting trained model.

---

1. Microsoft Learn Documentation

"Manage models in Azure Machine Learning": Under the section "Register and track models

" it states

"Registering a model allows you to store and version your models in the Azure cloud

in your workspace. The model registry makes it easy to organize and keep track of your trained models."

2. Microsoft Learn Documentation

"Train and register a model with the CLI (v2)": In the "Register the model" section

the documentation explains

"Now that you've trained the model

you can register it in your workspace... Each time you register a model with the same name

the registry assumes it's a new version."

3. Microsoft Learn Documentation

"What is MLOps?": In the "Model registration" section of the MLOps lifecycle diagram

it is defined as: "Model registration is the act of storing and versioning a model in your Azure Machine Learning workspace." This confirms registration is the key step for versioning.

Automated machine learning (automated ML or AutoML) in Azure Machine Learning is the specific capability designed to automate the iterative and time-consuming tasks of model development. It allows users to build, train, and deploy predictive models with high efficiency and productivity by automatically trying various algorithms and hyperparameter combinations. This process can be initiated through a user-friendly interface in the Azure Machine Learning studio, requiring minimal to no coding, which directly aligns with the requirement to "quickly build and deploy a predictive model without extensive coding."

A. ML pipelines are used to create and manage reproducible workflows for machine learning tasks, which typically requires coding and is focused on productionization and automation, not rapid initial model creation.

B. Copilot is a general term for an AI assistant that helps write code. While it can speed up coding, it does not eliminate the need for it in building a model.

C. DALL-E is a generative AI model within the Azure OpenAI Service used for creating images from text prompts, not for building predictive models from data.

1. Microsoft Learn

AI-900 Study Guide

"Describe automated machine learning": "Automated machine learning (often abbreviated to automated ML or AutoML) is a key capability in Azure Machine Learning that enables non-experts to create an effective machine learning model... It helps data scientists and developers save time by automating the iterative tasks..."

2. Microsoft Learn

"What is automated machine learning (AutoML)?": "Automated machine learning... is the process of automating the time-consuming

iterative tasks of machine learning model development. It allows data scientists

analysts

and developers to build ML models with high scale

efficiency

and productivity all while sustaining model quality."

3. Microsoft Learn

"What are Azure Machine Learning pipelines?": "An Azure Machine Learning pipeline is an independently executable workflow of a complete machine learning task... Pipelines are defined by using a Python SDK

or visually in Azure Machine Learning studio designer." This highlights that pipelines are for creating workflows

not the model itself in a no-code fashion.

4. Microsoft Learn

"What is Azure OpenAI Service?": Under the "Model summary" section

it lists "DALL-E models" as a "series of models that can generate original images from natural language." This confirms its purpose is image generation.

The chit-chat feature in QnA Maker (now part of Azure AI Language's Custom question answering) is specifically designed to make a bot more conversational and engaging. It allows you to add a pre-populated set of conversational responses for common small talk, such as greetings, goodbyes, and other pleasantries. You can select a specific personality for the bot, including "Professional," which directly addresses the user's need to add professional greetings and make the bot more user-friendly without having to manually create these question-and-answer pairs.

A. Increase the confidence threshold of responses: This adjusts how certain the service must be to return an answer. It filters out less relevant answers but does not add new conversational content like greetings.

B. Enable active learning: This feature suggests new question variations to add to your knowledge base based on user queries, improving the accuracy of existing answers, not adding general small talk.

C. Create multi-turn questions: This feature helps guide users to a specific answer through follow-up prompts. It is used for disambiguation within a topic, not for general, out-of-context greetings.

1. Microsoft Learn

"Add chit-chat to a project": "Adding chit-chat to your bot makes it more conversational and engaging. The chit-chat feature in custom question answering allows you to easily add a pre-populated set of the top chit-chat

to your project... When you create your project

you can add a chit-chat data source. You choose the personality you want for your chit-chat. The personalities available are: Professional

Friendly

Witty

Caring

and Enthusiastic."

2. Microsoft Learn

"Confidence score": "The confidence score indicates the confidence that the answer is the correct match for a given user query... By default the threshold is set to 0

so all possible answers are returned." This confirms its role as a filter

not a content source.

3. Microsoft Learn

"Active learning suggestions": "Active learning gives you suggestions for new question and answer pairs to add to your project based on user queries that are hitting your published project." This clarifies its purpose is to improve the existing knowledge base.

4. Microsoft Learn

"Use follow-up prompts to create multiple turns of a conversation": "Custom question answering lets you add follow-up prompts to question and answer pairs. This feature lets your client application provide a determined conversation flow with the user to reveal the correct answer." This shows its function is for guided conversations

not general greetings.

Conversational AI is defined by its ability to engage in a bidirectional dialogue, requiring components like Natural Language Understanding (NLU) to interpret intent and Natural Language Generation (NLG) to formulate a contextual response.

1. A webchat bot (Yes) is a prime example, as its core function is to understand user input and generate a relevant reply, thus maintaining a conversation.
2. A smart device (Yes) acting as a virtual assistant also fits this definition. It must understand the spoken query ("What will the weather be?"), process the intent, retrieve information, and generate a natural language answer.
3. Automatic captioning (No) is an application of Automatic Speech Recognition (ASR). ASR is a component used by conversational AI (to transcribe speech to text) but is not conversational AI by itself. It is a one-way transcription process and does not involve understanding intent or generating a dialogue-based response.

López, G., et al. (2020). "A review on conversational artificial intelligence." IEEE Access, vol. 8, pp. 137976-137994.DOI: https://doi.org/10.1109/ACCESS.2020.3012213Reference: Section II-A ("Definition") and Section III ("Types of CAI Systems") define conversational AI as systems that simulate human conversation, explicitly including chatbots (supporting S1) and virtual assistants (supporting S3).Ram, A., et al. (2018). "Conversational AI: The Science Behind the Technology." ACM Queue, vol. 16, no. 5.DOI: https://doi.org/10.1145/3283250.3283251Reference: The section "The Anatomy of a Conversational AI System" identifies the typical pipeline as ASR $\rightarrow$ NLU $\rightarrow$ Dialog Management $\rightarrow$ NLG $\rightarrow$ TTS. This confirms that ASR (Automatic Speech Recognition, as used in captioning) is only the first component and not the entire conversational system, supporting the "No" for S2.Massachusetts Institute of Technology (MIT). (2019). "Lecture 9: Chatbots." MIT 6.S198: Deep Learning.Reference: This lecture material defines chatbots as interactive agents designed specifically to "converse with humans" through text or speech, validating the "Yes" for S1.Badr, M., et al. (2022). "A Survey on Automatic Speech Recognition in Conversational AI." 2022 IEEE International Conference on Big Data (Big Data).DOI: https://doi.org/10.1109/BigData55660.2022.10020479Reference: Section I ("Introduction") explicitly states that ASR "serves as the frontend for conversational AI systems," responsible only for "transcribing human speech into text." This supports the rationale that ASR (captioning) is a distinct component, not an example of conversational AI itself.

App1 requires the extraction of text (brand names) from visual data (images/photos). Optical character recognition (OCR) is the precise AI technique designed for recognizing and extracting text from images, making it the most accurate choice over the broader Image Analysis. App2 describes a system for touchless access control, which universally relies on biometric identification, specifically Face recognition and verification, for authentication to grant entry. The Azure AI Vision Face service provides the necessary capabilities for identifying individuals to enable this functionality.

Microsoft Official Documentation (Azure AI Services - Vision):

Topic: Azure AI Vision Overview and Capabilities (OCR).

Reference: Official vendor documentation on the Read API or OCR, detailing its ability to "extract text from images and documents," which includes text like brand names. (Specific section on Optical Character Recognition).

IEEE Xplore Digital Library:

Topic: Face Recognition in Access Control Systems.

Reference: An academic paper discussing the application of face recognition algorithms (specifically detection, verification, and identification) for secure access systems. (e.g., IEEE Access, Section on Biometric Systems).

MIT OpenCourseWare (Courseware on Computer Vision):

Topic: Fundamentals of Optical Character Recognition.

Reference: Course materials defining OCR as the method for converting "scanned images of text into machine-encoded text," directly applicable to extracting text like brand names from photos. (e.g., Lecture notes on Document Analysis).

Cambridge University Press (Academic Publication on Biometrics):

Topic: Biometric Authentication for Physical Access.

Reference: Textbook chapter explaining that face recognition is a common, non-contact biometric method used for access control and security applications. (e.g., Chapter on Non-Contact Biometrics, pages 70-75).

Azure Form Recognizer is an AI service designed to extract text, key-value pairs, and tables from documents. It includes prebuilt models specifically trained for common document types like invoices and receipts. The service can identify and extract specific fields, such as an invoice number from an invoice or the retailer's name from a sales receipt. These capabilities make it the ideal solution for automating data entry from these types of forms.

B. Translating a form is a language translation task. The appropriate service for this is the Azure AI Translator service, not Form Recognizer, which focuses on data extraction.

C. Finding a product image in a catalog is an object detection or image analysis task. This would be handled by Azure AI Computer Vision or Custom Vision services.

1. Microsoft Learn

"What is Azure Form Recognizer?": This document states

"Azure Form Recognizer is a cloud-based Azure Applied AI Service that uses machine-learning models to extract key-value pairs

text

and tables from your documents." It explicitly lists invoices and receipts as primary use cases.

2. Microsoft Learn

"Form Recognizer prebuilt invoice model": Under the "Field extraction" section

this document lists InvoiceId as a standard field that the prebuilt invoice model extracts

directly supporting option A.

3. Microsoft Learn

"Form Recognizer prebuilt receipt model": The "Field extraction" section for the receipt model lists MerchantName as a key field it extracts from sales receipts

which corresponds to identifying the retailer in option D.

4. Microsoft Learn

"AI-900: Describe features of natural language processing (NLP) workloads": This module distinguishes between Azure AI services. It positions the Translator service for "translating text from one language to another" (refuting option B) and Form Recognizer for "extracting information from forms

receipts

and invoices."

This sequence represents the fundamental workflow for supervised machine learning, which is visually implemented in the Azure Machine Learning designer.

1. First, data must be ingested and prepared (e.g., cleaned, transformed).
2. Next, this dataset is split into a training set (to teach the model) and a validation/test set (to test it).
3. The model is then trained using only the training data.
4. Finally, the trained model's performance is assessed by evaluating it against the unseen validation dataset. Evaluating against the "original dataset" would be incorrect as it includes the training data, leading to an over-optimistic and invalid performance metric (overfitting).

Microsoft Press Store (for Exam DP-100): In the guide "Explore data and train models," the process for creating a training pipeline in the designer is detailed. It specifies the sequence:

Use modules to "clean and transform your data" (Prepare).

"Employ the Split Data module to divide your dataset into training and validation sets."

"Connect your preprocessed and split datasets to the Train Model module."

"Examine the output of the Evaluate Model module to assess the performance."

(Source: Microsoft Press Store, "Explore data and train models," Article 2, Skill 2.2: "Create models by using the Azure Machine Learning Designer," sections "Create a training pipeline" and "Here is a more detailed set of instructions...")

Microsoft Learn (Azure ML Documentation): The tutorial "Tutorial: Train a no-code regression model in the designer" demonstrates this logical flow. A dataset is added, prepared (e.g., Select Columns in Dataset, Clean Missing Data), and then split using the Split Data component. The outputs of the split data component are then fed into the Train Model component, which is subsequently connected to a Score Model and Evaluate Model component.

(Source: Microsoft Learn, "Tutorial: Train a no-code regression model in Azure Machine Learning designer," sections "Prepare data," "Split the data," "Train the model," and "Test, score, and evaluate the model.")

Microsoft Learn (Component Reference): The official documentation for the Train Model component states it has two inputs: an untrained algorithm and the "training dataset." It explicitly notes, "After training is completed, use the trained model with one of the scoring components, to make predictions on new data," which is the function of the Evaluate Model component.

(Source: Microsoft Learn, "Train Model: Component Reference," sections "How the training process works" and "How to use Train Model.")

The Azure AI Language service is the correct choice for determining a user's intent in a chatbot. Specifically, its Conversational Language Understanding (CLU) feature is designed to build custom natural language models. These models analyze conversational text to predict the user's intention (intent) and extract important information (entities). This capability is fundamental for a chatbot to understand what a user wants and to trigger the appropriate action or response. CLU is the designated service in Azure for building the core language understanding component of a conversational AI solution.

A. Translator: This service is used for translating text from one language to another; it does not analyze or determine the underlying intent of the text.

B. Azure Cognitive Search: This is an information retrieval service for indexing and querying large datasets. It finds relevant documents, but does not identify specific, pre-defined user intents for a conversational flow.

C. Speech: This service converts spoken audio to text (speech-to-text) and text to audible speech (text-to-speech). It processes the modality of communication, not the semantic meaning or intent.

---

1. Microsoft Learn

Azure AI Language Documentation. "What is conversational language understanding (CLU)?". This document states

"Conversational language understanding (CLU) is a cloud-based conversational AI service that applies custom machine-learning intelligence to a user's conversational

natural language text to predict overall meaning

and pull out relevant

detailed information." This directly confirms its purpose for identifying intent.

Source: Microsoft Azure Official Documentation

https://learn.microsoft.com/en-us/azure/ai-services/language-service/conversational-language-understanding/overview

Section: "What is conversational language understanding (CLU)?"

Paragraph 1.

2. Microsoft Learn

AI-900 Exam Study Guide. The official study guide for the AI-900 exam lists "Understand conversational language understanding" as a key skill measured. It places this capability within the "Azure AI Language" service

reinforcing that this is the correct service for building conversational solutions like chatbots.

Source: Microsoft Learn

https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-900/

Section: "Skills measured"

Subsection: "Describe features of natural language processing (NLP) workloads on Azure".

3. Microsoft Learn

Azure AI Speech Service Documentation. "What is the Speech service?". This document clarifies the service's function: "The Speech service provides speech-to-text and text-to-speech capabilities with a Speech resource." This confirms it does not handle intent recognition.

Source: Microsoft Azure Official Documentation

https://learn.microsoft.com/en-us/azure/ai-services/speech-service/overview

Section: "What is the Speech service?"

Paragraph 1.