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AI-900 Practice Question: Describe features of computer vision workloads on Azure
Drag and drop the steps to process text with Azure Text Analytics (Language service) into the correct order.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Step 1: Provision an Azure Text Analytics resource, then Step 2: Retrieve the endpoint and key, then Step 3: Instantiate the Text Analytics client, then Step 4: Submit a document for analysis and process the response.
Using Text Analytics involves setting up a resource, making an API call, and interpreting results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Step 1: Provision an Azure Text Analytics resource, then Step 2: Retrieve the endpoint and key, then Step 3: Instantiate the Text Analytics client, then Step 4: Submit a document for analysis and process the response.
Why this is correct
In the correct sequence, you first provision an Azure Text Analytics (Azure AI Language) resource in your subscription, which is what generates the service's endpoint and access keys. After provisioning, you retrieve these credentials, because they establish authenticated access to the API. You then instantiate the Text Analytics client using the endpoint and key, and finally submit a document through that client to analyze sentiment, key phrases, entities, or language and process the returned result.
- ✗
Step 2: Retrieve the endpoint and key, then Step 1: Provision the resource, then Step 3: Instantiate the client, then Step 4: Submit a document.
Why it's wrong here
This order is invalid because retrieving the endpoint and key must happen after the Text Analytics resource is provisioned, since the provisioning step aggregates and creates those credentials. A request for an endpoint or key before any resource exists would return a 404 or an empty result, because there is no service instance to attribute them to. Only after provisioning can you retrieve the credentials and continue with authentication.
- ✗
Step 1: Provision the resource, then Step 3: Instantiate the client, then Step 2: Retrieve the endpoint and key, then Step 4: Submit a document.
Why it's wrong here
This arrangement places client instantiation before credential retrieval, but the Text Analytics client constructor requires both an endpoint and a key/credential object, so it will fail with a credential or configuration error if those values are not yet available. You cannot meaningfully create a connected client without the resource's endpoint and key; authentication is a prerequisite for client creation. The correct dependency is that credentials must exist and be retrieved by the SDK before the client is built.
- ✗
Step 1: Provision the resource, then Step 2: Retrieve the endpoint and key, then Step 4: Submit a document, then Step 3: Instantiate the client.
Why it's wrong here
This order tries to submit a document before instantiating the client, but the Text Analytics SDK's analysis methods are instance methods on the authenticated client object, so there is no way to send a request without first creating that client. The client holds the service endpoint, credential, and configuration such as retry policy and regional endpoint; skipping instantiation leaves you without an object to execute the HTTP call. Therefore, client creation must precede document submission.
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Azure Machine Learning Studio
Key term
Service
A service is a software component or system that performs a specific function and is available to be used by other programs or users over a network.
Key term
Text analytics
Text analytics is the process of turning unstructured text, like emails or social media posts, into structured data that can be analyzed to find patterns, sentiments, and insights.
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