- A
Use the prebuilt named entity recognition (NER) to identify entities related to financial terms, then apply sentiment analysis to the entire transcript to determine overall optimism.
Why wrong: Prebuilt NER does not detect forward-looking statements; sentiment analysis on the whole transcript does not meet the per-statement requirement.
- B
Build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence.
Custom NER can be trained to identify forward-looking statements; custom text classification can then analyze each statement for optimism and disclaimer. Both are available in Azure AI Language and require only labeled data, not custom ML training.
- C
Use custom question answering to create a knowledge base of typical forward-looking statements and query the transcript for matches.
Why wrong: Question answering is designed for answering questions based on a knowledge base, not for extracting and classifying statements from text. It would be inefficient and miss many statements.
- D
Use key phrase extraction to identify important phrases, then run sentiment analysis on each sentence to detect optimism.
Why wrong: Key phrases are not structured statements; sentence-level sentiment does not capture the presence of disclaimers. This approach would be inaccurate and require additional parsing.
Quick Answer
The answer is to build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence. This approach is correct because Azure AI Language’s custom NER can be trained on labeled transcript excerpts to identify phrases like “we expect revenue to grow,” while custom text classification can then analyze each extracted statement for optimistic language and disclaimer inclusion—all without training a machine learning model from scratch, relying instead on Azure’s pre-built labeling and training pipeline. On the AI-102 exam, this scenario tests your ability to combine two custom language features for a multi-step NLP pipeline, and the common trap is choosing prebuilt entity recognition or sentiment analysis, which cannot isolate forward-looking statements or provide per-statement scores. Remember the memory tip: “Extract with NER, then classify with text classification—never mix sentiment for extraction.”
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.
You are a developer at a large financial institution. The compliance team needs to automatically analyze quarterly earnings call transcripts to extract forward-looking statements (e.g., 'we expect revenue to grow') and flag any that are overly optimistic or lack necessary disclaimers. The transcripts are stored as text files in Azure Blob Storage. You need to design a solution using Azure AI Language services that meets the following requirements: 1) Extract all forward-looking statements from each transcript. 2) For each statement, determine if it contains optimistic language (e.g., 'strong growth', 'excellent performance') and if it includes a disclaimer (e.g., 'this is a forward-looking statement'). 3) Output a structured JSON file per transcript with the statements, optimism score, and disclaimer presence. 4) Minimize development effort and avoid custom machine learning model training. Which approach should you take?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence.
Option B is correct because it combines custom NER to extract forward-looking statements and custom text classification to classify each statement for optimism and disclaimer presence, both using Azure AI Language's pre-built capabilities that require only labeling effort. Option A is wrong because the prebuilt entity recognition does not include forward-looking statements as an entity type. Option C is wrong because key phrase extraction cannot identify specific statements, and sentiment analysis gives an overall score, not per-statement. Option D is wrong because question answering is designed for Q&A, not extraction and classification of statements.
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.
- ✗
Use the prebuilt named entity recognition (NER) to identify entities related to financial terms, then apply sentiment analysis to the entire transcript to determine overall optimism.
Why it's wrong here
Prebuilt NER does not detect forward-looking statements; sentiment analysis on the whole transcript does not meet the per-statement requirement.
- ✓
Build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence.
Why this is correct
Custom NER can be trained to identify forward-looking statements; custom text classification can then analyze each statement for optimism and disclaimer. Both are available in Azure AI Language and require only labeled data, not custom ML training.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use custom question answering to create a knowledge base of typical forward-looking statements and query the transcript for matches.
Why it's wrong here
Question answering is designed for answering questions based on a knowledge base, not for extracting and classifying statements from text. It would be inefficient and miss many statements.
- ✗
Use key phrase extraction to identify important phrases, then run sentiment analysis on each sentence to detect optimism.
Why it's wrong here
Key phrases are not structured statements; sentence-level sentiment does not capture the presence of disclaimers. This approach would be inaccurate and require additional parsing.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Trap categories for this question
Keyword trap
Key phrases are not structured statements; sentence-level sentiment does not capture the presence of disclaimers. This approach would be inaccurate and require additional parsing.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Build a custom NER model to extract forward-looking statements, then use a custom text classification model to classify each extracted statement for optimism and disclaimer presence. — Option B is correct because it combines custom NER to extract forward-looking statements and custom text classification to classify each statement for optimism and disclaimer presence, both using Azure AI Language's pre-built capabilities that require only labeling effort. Option A is wrong because the prebuilt entity recognition does not include forward-looking statements as an entity type. Option C is wrong because key phrase extraction cannot identify specific statements, and sentiment analysis gives an overall score, not per-statement. Option D is wrong because question answering is designed for Q&A, not extraction and classification of statements.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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