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| Section | Objectives |
|---|---|
| Topic 1: Implement Computer Vision Solutions | - Analyze images and videos using Azure AI Vision - Implement custom vision models - Extract information using OCR and form recognition |
| Topic 2: Implement Generative AI Solutions | - Build retrieval-augmented generation (RAG) solutions - Integrate prompt engineering techniques - Implement Azure OpenAI Service solutions |
| Topic 3: Implement Natural Language Processing Solutions | - Implement question answering and conversational AI - Analyze text using Azure AI Language - Translate and interpret language using Azure AI services |
| Topic 4: Plan and Manage an Azure AI Solution | - Manage security, compliance, and responsible AI - Select appropriate Azure AI services - Plan solution requirements and architecture |
| Topic 5: Implement Knowledge Mining and Document Intelligence | - Build Azure AI Search solutions - Enrich and index data for search scenarios - Implement document intelligence solutions |
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NEW QUESTION # 203
Match the types of workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may he used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
There are two main types of data workloads: Batch and Streaming.
* Batch Workload
* Processes data in large groups (batches) at scheduled intervals.
* Common when real-time is not required.
* Examples: daily sales reports, hourly data refreshes, periodic ETL (Extract, Transform, Load).
* Streaming Workload
* Processes data in real time or near real time as it arrives.
* Used for scenarios where immediate insights or actions are required.
* Examples: fraud detection, IoT sensor monitoring, live purchase tracking.
* "Data for a product catalog will be loaded every 12 hours to a data warehouse."
* This is scheduled, not continuous. # Batch
* "Thousands of data sets per second for online purchases will be loaded into a data warehouse in real time."
* This requires real-time processing. # Streaming
* "Updates to inventory data will be loaded to a data warehouse every 1 million transactions."
* Data is grouped and loaded after reaching a threshold (1 million). # Batch Correct Matches:
* Product catalog (12 hours) # Batch
* Online purchases (real time) # Streaming
* Inventory data (1 million transactions) # Batch
* Batch processing vs. Stream processing in Azure
* Azure Stream Analytics overview
* Data processing approaches: Batch vs Real-time
NEW QUESTION # 204
You are developing a text processing solution.
You have the function shown below.
Answer:
Explanation:
Explanation:
NEW QUESTION # 205
Match the Azure Cosmos DB APIs to the appropriate data structures.
To answer, drag the appropriate API from the column on the left to its data structure on the right. Each API may be used once, more than once, or not at all.
NOTE: Each correct match is worth one point.
Answer:
Explanation:
Explanation:
Azure Cosmos DB supports multiple APIs, each aligned to a specific data model:
* Gremlin API
* Designed for graph data.
* Stores vertices (nodes) and edges (relationships).
* Query language: Gremlin.
* Correct mapping: Graph data.
* MongoDB API
* Designed for document data using JSON.
* Supports MongoDB wire protocol.
* Correct mapping: JSON documents.
* Table API
* Designed for key-value data.
* Compatible with Azure Table Storage.
* Correct mapping: Key/value data.
* Cassandra API
* Designed for wide-column (column-family) data.
* Uses CQL (Cassandra Query Language).
* Not listed in the answer area, but if included, it maps to columnar data, not the options shown here.
Correct Mapping:
* Graph data # Gremlin API
* JSON documents # MongoDB API
* Key/value data # Table API
* APIs in Azure Cosmos DB
* Choose the right API for Azure Cosmos DB
NEW QUESTION # 206
You build a Language Understanding model by using the Language Understanding portal.
You export the model as a JSON file as shown in the following sample.
To what does the Weather.Historic entity correspond in the utterance?
Answer: C
Explanation:
Let's carefully analyze the JSON output from the exported LUIS (Language Understanding) model.
The utterance is:
" average amount of rain by month at chicago last year "
The extracted entities are:
Weather.WeatherRange # " average " (positions 0-6)
Weather.WeatherCondition # " rain " (positions 18-21)
Weather.Historic # " by month " (positions 23-30)
We need to determine what text fragment corresponds to Weather.Historic .
Looking at the " startPos " : 23, " endPos " : 30 , these indexes map to the substring " by month " in the utterance.
Why not the other options?
B). chicago # Refers to a location entity, but the entity name is Weather.Location (not shown here).
C). rain # Already captured as Weather.WeatherCondition .
D). location # Would also be "chicago," but again that's a different entity type.
So the only correct match for Weather.Historic is " by month " .
The answer: A. by month
Microsoft References
Entities in LUIS
Export LUIS model schema
Labeling utterances with entities
NEW QUESTION # 207
You are developing an application that includes language translation.
The application will translate text retrieved by using a function named getTextToBeTranslated. The text can be in one of many languages. The content of the text must remain within the Americas Azure geography.
You need to develop code to translate the text to a single language.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 208
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