The correct answer is that one of the reviews exceeds the maximum character limit for a single document. Azure Cognitive Service for Language enforces a hard cap of 5,120 characters per document for key phrase extraction; when a review surpasses this threshold, the API rejects the entire request with a 400 Bad Request error because the payload violates the service’s input constraints. On the AI-102 exam, this scenario tests your understanding of service quotas and error handling in batch text analysis operations—a common trap is assuming the error stems from authentication or language support rather than document size. Remember the mnemonic “5K for Key Phrases” to recall that 5,120 characters is the ceiling, and any text longer than that will trigger a 400 error before processing even begins.
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
A development team is using Azure Cognitive Service for Language to extract key phrases from customer reviews. They notice that some reviews are not being processed, and the API returns a 400 error code. What is the most likely cause?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
One of the reviews exceeds the maximum character limit for a single document.
The Azure Cognitive Service for Language key phrase extraction API enforces a maximum document size of 5,120 characters per document. When a single review exceeds this limit, the API returns a 400 Bad Request error because the request payload violates the service's input constraints. This is the most common cause of 400 errors in batch text analysis operations.
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.
✓
One of the reviews exceeds the maximum character limit for a single document.
Why this is correct
The service limits each document to 5,120 characters.
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.
✗
The reviews contain characters that are not valid UTF-8.
Why it's wrong here
Invalid encoding would cause a different error, typically a 415 or 400 with a specific message.
✗
The request contains more than 5 documents.
Why it's wrong here
The maximum is 10 documents, so 5 is acceptable.
✗
The reviews are written in a language not supported by the service.
Why it's wrong here
The service supports many languages; a 400 error due to language is unlikely.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume the 400 error is due to unsupported languages or encoding issues, but the actual constraint is the per-document character limit, which is explicitly documented in the service's input specifications.
Detailed technical explanation
How to think about this question
Under the hood, the key phrase extraction endpoint uses a pre-trained transformer model that processes each document as a sequence of tokens. The 5,120-character limit is based on the model's maximum input token length (approximately 128,000 tokens for GPT-4-based models, but the legacy service caps at 5,120 characters to ensure consistent latency and cost). In real-world scenarios, truncating long reviews before submission or splitting them into multiple documents avoids this error while preserving analytical value.
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.
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: One of the reviews exceeds the maximum character limit for a single document. — The Azure Cognitive Service for Language key phrase extraction API enforces a maximum document size of 5,120 characters per document. When a single review exceeds this limit, the API returns a 400 Bad Request error because the request payload violates the service's input constraints. This is the most common cause of 400 errors in batch text analysis operations.
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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Question Discussion
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