- A
Use Azure OpenAI Service to analyze sentiment and generate summaries, and store results in a Cosmos DB for querying.
Why wrong: This requires custom coding to process data and build the query interface; also, Azure OpenAI is not optimized for structured analytics.
- B
Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.
Azure Cognitive Search can index the feedback with extracted metadata, and its semantic search can interpret natural language queries like 'complaints about shipping' and return relevant documents.
- C
Use Azure AI Language to perform sentiment analysis and key phrase extraction, then load the results into Power BI for trend analysis and natural language Q&A.
Why wrong: Power BI's Q&A is limited to data in its model; it cannot directly query raw text. The analyst wants to ask questions about the original feedback, not just the extracted metrics.
- D
Use Azure AI Language's custom question answering to create a knowledge base from the feedback and allow natural language queries.
Why wrong: Question answering is designed for static FAQ knowledge bases, not for dynamic querying over a large corpus of feedback with sentiment and trend analysis.
Quick Answer
The correct combination is Azure AI Language for sentiment and key phrase extraction, paired with Azure Cognitive Search with semantic search enabled for indexing and natural language querying. This works because Azure AI Language processes the raw text from Blob Storage to detect sentiment trends over time and extract key topics, while Cognitive Search’s semantic ranker allows business analysts to ask free-form questions like “Show me complaints about shipping in the last month” without custom code. On the AI-102 exam, this scenario tests your understanding of how to chain cognitive services with search for text analytics and conversational querying—a common trap is choosing Power BI for natural language, but Power BI lacks direct querying over indexed text. Remember the memory tip: “Language extracts, Search queries” to keep the pipeline straight.
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 building a solution to analyze customer feedback from multiple sources: emails, chat logs, and survey responses. You need to detect the overall sentiment trend over time and identify the most frequently mentioned topics. The solution must also allow the business analyst to ask natural language questions about the data (e.g., 'Show me complaints about shipping in the last month'). You have all data in Azure Blob Storage. You need to implement a solution with minimal custom code. Which combination of Azure services should you use?
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
Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.
Option C is correct because Azure AI Language can perform sentiment analysis and key phrase extraction, and Azure Cognitive Search with semantic ranker can enable natural language querying over the indexed data. Option A is wrong because Azure OpenAI Service alone does not provide built-in analytics over stored data. Option B is wrong because Power BI is for visualization, not natural language querying over text. Option D is wrong because Azure AI Language's question answering is for FAQ, not for querying indexed data.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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 Azure OpenAI Service to analyze sentiment and generate summaries, and store results in a Cosmos DB for querying.
Why it's wrong here
This requires custom coding to process data and build the query interface; also, Azure OpenAI is not optimized for structured analytics.
- ✓
Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries.
Why this is correct
Azure Cognitive Search can index the feedback with extracted metadata, and its semantic search can interpret natural language queries like 'complaints about shipping' and return relevant documents.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Use Azure AI Language to perform sentiment analysis and key phrase extraction, then load the results into Power BI for trend analysis and natural language Q&A.
Why it's wrong here
Power BI's Q&A is limited to data in its model; it cannot directly query raw text. The analyst wants to ask questions about the original feedback, not just the extracted metrics.
- ✗
Use Azure AI Language's custom question answering to create a knowledge base from the feedback and allow natural language queries.
Why it's wrong here
Question answering is designed for static FAQ knowledge bases, not for dynamic querying over a large corpus of feedback with sentiment and trend analysis.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
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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 — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use Azure AI Language to extract sentiment and key phrases, then index the data in Azure Cognitive Search with semantic search enabled; use the search's built-in features for trend analysis and natural language queries. — Option C is correct because Azure AI Language can perform sentiment analysis and key phrase extraction, and Azure Cognitive Search with semantic ranker can enable natural language querying over the indexed data. Option A is wrong because Azure OpenAI Service alone does not provide built-in analytics over stored data. Option B is wrong because Power BI is for visualization, not natural language querying over text. Option D is wrong because Azure AI Language's question answering is for FAQ, not for querying indexed data.
What should I do if I get this AI-102 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI-102 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
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Last reviewed: Jun 20, 2026
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.
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