Designing and Implementing an Azure AI 試験
Question No : 1
You are designing an Azure Batch AI solution that will be used to train many different Azure Machine Learning models.
The solution will perform the following:
- Image recognition
- Deep learning that uses convolutional neural networks.
You need to select a compute infrastructure for each model. The solution must minimize the processing time.
What should you use for each model? To answer, drag the appropriate compute infrastructures to the correct models. Each compute infrastructure may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Question No : 2
You are designing a solution that will analyze bank transactions in real time. The transactions will be evaluated by using an algorithm and classified into one of five groups. The transaction data will be enriched with information taken from Azure SQL Database before the transactions are sent to the classification process. The enrichment process will require custom code. Data from different banks will require different stored procedures.
You need to develop a pipeline for the solution.
Which components should you use for data ingestion and data preparation? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Question No : 3
You are designing an AI application that will use an azure Machine Learning Studio experiment.
The source data contains more than 200 TB of relational tables. The experiment will run once a month.
You need to identify a data storage solution for the application. The solution must minimize compute costs.
Which data storage solution should you identify?
Question No : 4
Question Set 1
Your company has a data team of Scala and R experts.
You plan to ingest data from multiple Apache Kafka streams.
You need to recommend a processing technology to broker messages at scale from Kafka streams to Azure Storage.
What should you recommend?
Question No : 5
Contoso, Ltd. has an office in New York to serve its North American customers and an office in Paris to serve its European customers.
Each office has a small data center that hosts Active Directory services and a few off-the-shelf software solutions used by internal users.
The network contains a single Active Directory forest that contains a single domain named contoso.com. Azure Active Directory (Azure AD) Connect is used to extend identity management to Azure.
The company has an Azure subscription. Each office has an Azure ExpressRoute connection to the subscription. The New York office connects to a virtual network hosted in the US East 2 Azure region. The Paris office connects to a virtual network hosted in the West Europe Azure region.
The New York office has an Azure Stack Development Kit (ASDK) deployment that is used for development and testing.
Current Business Model
Contoso has a web app named Bookings hosted in an App Service Environment (ASE). The ASE is in the virtual network in the East US 2 region. Contoso employees and customers use Bookings to reserve hotel rooms.
Bookings connects to a Microsoft SQL Server database named hotelDB in the New York office.
The database has a view named vwAvailability that consolidates columns from the tables named Hotels, Rooms, and RoomAvailability. The database contains data that was collected during the last 20 years.
Contoso identifies the following issues with its current business model:
- European users report that access to Booking is slow, and they lose customers who must wait on the phone while they search for available rooms.
- Users report that Bookings was unavailable during an outage in the New York data center for more than 24 hours.
Contoso identifies the following issues with its current business model:
- European users report that access to Bookings is slow, and they lose customers who must wait on the phone while they search for available rooms.
- Users report that Bookings was unavailable during on outage in the New York data center for more than 24 hours.
Contoso wants to provide a new version of the Bookings app that will provide a highly available, reliable service for booking travel packages by interacting with a chatbot named Butler.
Contoso plans to move all production workloads to the cloud.
Contoso identifies the following technical requirements:
- Data scientists must test Butler by using ASDK.
- Whenever possible, solutions must minimize costs.
- Butler must greet users by name when they first connect.
- Butler must be able to handle up to 10,000 messages a day.
- Butler must recognize the users' intent based on basic utterances.
- All configurations to the Azure Bot Service must be logged centrally.
- Whenever possible, solutions must use the principle of least privilege.
- Internal users must be able to access Butler by using Microsoft Skype for Business.
- The new Bookings app must provide a user interface where users can interact with Butler.
- Users in an Azure AD group named KeyManagers must be able to manage keys for all Azure Cognitive Services.
- Butler must provide users with the ability to reserve a room, cancel a reservation, and view existing reservations.
- The new Bookings app must be available to users in North America and Europe if a single data center or Azure region fails.
- For continuous improvement, you must be able to test Butler by sending sample utterances and comparing the chatbot's responses to the actual intent.
- You must maintain relationships between data after migration.
You need to recommend a data storage solution that meets the technical requirements.
What is the best data storage solution to recommend? More than one answer choice may achieve the goal. Select the BEST answer.
Question No : 6
You are designing a real-time speech-to-text AI feature for an Android mobile app. The feature will stream data to the Speech service.
You need to recommend which audio format to use to serialize the audio. The solution must minimize the amount of data transferred to the cloud.
What should you recommend?
Currently, only the following configuration is supported: Audio samples in PCM format, one channel, 16 bits per sample, 8000 or 16000 samples per second (16000 or 32000 bytes per second), two block align (16 bit including padding for a sample).
Question No : 7
You are designing an AI solution that will analyze millions of pictures by using Azure HDInsight Hadoop cluster.
You need to recommend a solution for storing the pictures. The solution must minimize costs.
Which storage solution should you recommend?
Azure Data Lake Store is optimized for storing large amounts of data for reporting and analytical and is geared towards storing data in its native format, making it a great store for non-relational data.
Question No : 8
You are developing a mobile application that will perform optical character recognition (OCR) from photos.
The application will annotate the photos by using metadata, store the photos in Azure Blob storage, and then score the photos by using an Azure Machine Learning model.
What should you use to process the data?
Question No : 9
You plan to create an intelligent bot to handle internal user chats to the help desk of your company.
The bot has the following requirements:
- Must be able to interpret what a user means.
- Must be able to perform multiple tasks for a user.
- Must be able to answer questions from an existing knowledge base.
You need to recommend which solutions meet the requirements.
Which solution should you recommend for each requirement? To answer, drag the appropriate solutions to the correct requirements. Each solution may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Box 1: The Language Understanding (LUIS) service
Language Understanding (LUIS) is a cloud-based API 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.
Box 2: Text Analytics API
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 3: The QnA Maker service
QnA Maker is a cloud-based Natural Language Processing (NLP) service that easily creates a natural conversational layer over your data. It can be used to find the most appropriate answer for any given natural language input, from your custom knowledge base (KB) of information.
Dispatch tool library:
If a bot uses multiple LUIS models and QnA Maker knowledge bases (knowledge bases), you can use Dispatch tool to determine which LUIS model or QnA Maker knowledge base best matches the user input. The dispatch tool does this by creating a single LUIS app to route user input to the correct model.
Question No : 10
Your company plans to monitor twitter hashtags, and then to build a graph of connected people and places that contains the associated sentiment.
The monitored hashtags use several languages, but the graph will be displayed in English.
You need to recommend the required Azure Cognitive Services endpoints for the planned graph.
Which Cognitive Services endpoints should you recommend?
Sentiment analysis, which is also called opinion mining, uses social media analytics tools to determine attitudes toward a product or idea.
Translator Text: Translate text in real time across more than 60 languages, powered by the latest innovations in machine translation.
The Key Phrase Extraction skill evaluates unstructured text, and for each record, returns a list of key phrases. This skill uses the machine learning models provided by Text Analytics in Cognitive Services. This capability is useful if you need to quickly identify the main talking points in the record. For example, given input text "The food was delicious and there were wonderful staff", the service returns "food" and "wonderful staff".
Question No : 11
You plan to build an app that will provide users with the ability to dictate messages and convert the messages into text.
You need to recommend a solution to meet the following requirements for the app:
- Must be able to transcribe streaming dictated messages that are longer than 15 seconds.
- Must be able to upload existing recordings to Azure Blob storage to be transcribed later.
Which solution should you recommend for each requirement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Box 1: The Speech SDK
The Speech SDK is not limited to 15 seconds.
Box 2: Batch Transcription API
Batch transcription is a set of REST API operations that enables you to transcribe a large amount of audio in storage. You can point to audio files with a shared access signature (SAS) URI and asynchronously receive transcription results. With the new v3.0 API, you have the choice of transcribing one or more audio files, or process a whole storage container.
Asynchronous speech-to-text transcription is just one of the features.
Question No : 12
Your company manages a sports team.
The company sets up a video booth to record messages for the team.
Before replaying the messages on a video screen, you need to generate captions for the messages and check the sentiment of the video to ensure that only positive messages are played.
Which Azure Cognitive Services service should you use?
Video Indexer includes Audio transcription: Converts speech to text in 12 languages and allows extensions. Supported languages include English, Spanish, French, German, Italian, Mandarin Chinese, Japanese, Arabic, Russian, Portuguese, Hindi, and Korean.
When indexing by one channel, partial result for those models will be available, such as sentiment analysis: Identifies positive, negative, and neutral sentiments from speech and visual text.
Question No : 13
You have an on-premises repository that contains 5,000 videos. The videos feature demonstrations of the products sold by your company.
The company’s customers plan to search the videos by using the name of the product demonstrated in each video.
You need to build a custom search tool for the customers.
What should you do first?
Azure Media Services can be used to encode and package content, stream videos on-demand, broadcast live, analyze your videos with Media Services v3. You can snalyze recorded videos or audio content. For example, to achieve higher customer satisfaction, organizations can extract speech-to-text and build search indexes and dashboards. Then, they can extract intelligence around common complaints, sources of complaints, and other relevant data.
Question No : 14
You have an existing Language Understanding (LUIS) model for an internal bot.
You need to recommend a solution to add a meeting reminder functionality to the bot by using a prebuilt model. The solution must minimize the size of the model.
Which component of LUIS should you recommend?
LUIS includes a set of prebuilt entities for recognizing common types of information, like dates, times, numbers, measurements, and currency. Prebuilt entity support varies by the culture of your LUIS app.
Note: LUIS provides three types of prebuilt models. Each model can be added to your app at any time.
Model type: Includes
- Domain: Intents, utterances, entities
- Intents: Intents, utterances
- Entities: Entities only
Question No : 15
Your company plans to create a mobile app that will be used by employees to query the employee handbook.
You need to ensure that the employees can query the handbook by typing or by using speech.
Which core component should you use for the app?
Azure Cognitive Search (formerly known as "Azure Search") is a search-as-a-service cloud solution that gives developers APIs and tools for adding a rich search experience over private, heterogeneous content in web, mobile, and enterprise applications. Your code or a tool invokes data ingestion (indexing) to create and load an index. Optionally, you can add cognitive skills to apply AI processes during indexing. Doing so can add new information and structures useful for search and other scenarios.
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.