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Implement natural language processing solutions →mediumMultiple ChoiceObjective-mapped

AI-102 Practice Question: Implement natural language processing solutions

This AI-102 practice question tests your understanding of implement natural language processing solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

Exhibit

{
  "parameters": {
    "projectName": "InvoiceExtractor",
    "deploymentName": "production",
    "api-version": "2023-04-01",
    "body": {
      "analysisInput": {
        "documents": [
          {"id": "1", "language": "en", "text": "Invoice #1234 dated 01/15/2023 for $500.00 from Acme Corp."}
        ]
      },
      "parameters": {
        "modelVersion": "latest"
      }
    }
  }
}

Refer to the exhibit. You send this request to the Azure AI Language Service for custom entity recognition. The response returns no entities. What is the most likely reason?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

  • Clue: "most likely"

    Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

Exhibit

{
  "parameters": {
    "projectName": "InvoiceExtractor",
    "deploymentName": "production",
    "api-version": "2023-04-01",
    "body": {
      "analysisInput": {
        "documents": [
          {"id": "1", "language": "en", "text": "Invoice #1234 dated 01/15/2023 for $500.00 from Acme Corp."}
        ]
      },
      "parameters": {
        "modelVersion": "latest"
      }
    }
  }
}

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

✓

The projectName and deploymentName are missing from the body parameters

Option D is correct because the Azure AI Language Service for custom entity recognition requires both `projectName` and `deploymentName` in the request body to identify which trained custom model to invoke. Without these parameters, the service cannot route the request to the correct custom model, so it returns no entities. The standard pre-built entity recognition does not require these fields, but custom entity recognition mandates them.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • ✗

    The text input is too short for entity recognition

    Why it's wrong here

    The input length is sufficient.

  • ✗

    The language parameter is set incorrectly

    Why it's wrong here

    The language 'en' is valid.

  • ✗

    The model version is not specified

    Why it's wrong here

    It is set to 'latest'.

  • ✓

    The projectName and deploymentName are missing from the body parameters

    Why this is correct

    Custom entity recognition requires project and deployment names in the body.

    Clue confirmation

    The clue word "most likely" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume the request is valid because it includes text and a language parameter, overlooking that custom entity recognition requires explicit project and deployment identifiers to invoke the trained model.

Detailed technical explanation

How to think about this question

Under the hood, the custom entity recognition endpoint uses a REST API call to `POST /language/analyze-text/jobs` with a JSON body that must include `parameters.projectName` and `parameters.deploymentName`. These fields point to the specific trained model deployed in your Language Studio project. If omitted, the service interprets the request as a standard pre-built entity recognition task, which may not match the custom entities you trained, leading to an empty response. In a real-world scenario, a developer might forget to include these fields when migrating from pre-built to custom entity recognition, causing silent failures.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this AI-102 question test?

Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The projectName and deploymentName are missing from the body parameters — Option D is correct because the Azure AI Language Service for custom entity recognition requires both `projectName` and `deploymentName` in the request body to identify which trained custom model to invoke. Without these parameters, the service cannot route the request to the correct custom model, so it returns no entities. The standard pre-built entity recognition does not require these fields, but custom entity recognition mandates them.

What should I do if I get this AI-102 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jul 4, 2026

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This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.