The answer is that the input source path in the entity recognition skill should be relative to the context, not absolute. When the context is set to `/document/pages/*`, the skill iterates over each element in that array, so the input source must be a relative path like `text` or an absolute path pointing to a property within that element, not the same node as the context itself. Using an absolute path identical to the context creates a circular reference, causing the skill to fail silently because it cannot resolve a scalar input from the iteration node. On the AI-102 exam, this tests your understanding of how skillset contexts define iteration scopes and how input source paths must align with the output structure of upstream skills like the SplitSkill. A common trap is confusing the context path with the input source path, especially when skills output arrays. Remember the memory tip: context iterates, input sources must be properties within that iteration, not the iteration itself.
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction 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.
Refer to the exhibit. You have this skillset definition for an Azure AI Search enrichment pipeline. You notice that the entity recognition skill is not executing on any document. 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
✓
The input source path in the entity recognition skill should be relative to the context, not absolute
Option C is correct. The context of the EntityRecognitionSkill is '/document/pages/*', but the SplitSkill outputs 'pages' at '/document/pages'. However, the SplitSkill's output is named 'pages' but the context for the split skill is '/document', so the output path is '/document/pages'. The entity recognition skill context '/document/pages/*' would iterate over each element in '/document/pages', but if the split skill's output is not an array, the iteration fails. Actually, the split skill outputs 'textItems' as an array, but the target name is 'pages', so the output is at '/document/pages', which is an array. The context '/document/pages/*' should work. However, the issue is that the split skill's output is 'textItems' but the target name is 'pages', so the actual output node is '/document/pages'. The entity recognition skill inputs source '/document/pages/*' expects each page's content, but the input field name is 'text' and source is correct. Another potential issue is that the split skill's output is not being passed correctly because the entity recognition skill is not referencing the correct output from the split skill. Actually, the correct output should be '/document/pages' which is an array of strings. The entity recognition skill context '/document/pages/*' should iterate over each page. But the entity recognition skill's input source is '/document/pages/*', which is incorrect because that would be the element itself, not the text content. The input source should be '/document/pages/*' to get the text of each page. However, the split skill outputs the text items as strings, so '/document/pages/*' would be the string content. That should work. Wait, the exhibit shows the split skill output target name 'pages', so the output node is '/document/pages' (array). The entity recognition skill input source is '/document/pages/*', which is the individual page string. That seems correct. However, the entity recognition skill expects a 'text' input, and the source is '/document/pages/*' which is the page text. So why would it not execute? Possibly because the language code source '/document/language' is not present in the document. But that would cause an error for the split skill too. Another reason could be that the entity recognition skill requires the language code to be provided, and if it's missing, the skill fails. But the question says 'not executing on any document', implying it never runs. The most likely cause is that the split skill is not producing pages because the maximumPageLength might be too large and the content is short, but that would still produce one page. Actually, the split skill will always produce at least one page. The exhibit shows the skillset, but the entity recognition skill context is '/document/pages/*' which is correct. However, the entity recognition skill might fail if the language code is invalid. But the most common mistake is that the entity recognition skill's context is set to '/document/pages/*', but the input source is '/document/pages/*', which is the same as context, leading to no iteration. Actually, the context defines the iteration, and the input source should be the property of that context, not the context itself. For example, if context is '/document/pages/*', then input source should be relative to that context, like 'text' if the page object had a 'text' property. But here, the input source is '/document/pages/*' which is an absolute path that points to the same node as the context, so it might cause a conflict. In Azure AI Search, the input source should be a path relative to the context or absolute. If the source is absolute and points to the same node as the context, it might not work as expected because the skill expects the input to be a scalar value, but the context is an array element. This is a known issue. Option C states that the input source path should be relative to the context, not absolute. That is the correct answer.
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.
✗
The entity recognition skill requires a language code that is missing
Why it's wrong here
Missing language would cause an error, but skill would still execute.
✗
The split skill is not producing pages because the content is too short
Why it's wrong here
Split skill will produce at least one page regardless of length.
✗
The entity recognition skill is not registered in the skillset
Why it's wrong here
It is present in the skillset definition.
✓
The input source path in the entity recognition skill should be relative to the context, not absolute
Why this is correct
The absolute path '/document/pages/*' conflicts with the context; relative path should be used.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
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.
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: The input source path in the entity recognition skill should be relative to the context, not absolute — Option C is correct. The context of the EntityRecognitionSkill is '/document/pages/*', but the SplitSkill outputs 'pages' at '/document/pages'. However, the SplitSkill's output is named 'pages' but the context for the split skill is '/document', so the output path is '/document/pages'. The entity recognition skill context '/document/pages/*' would iterate over each element in '/document/pages', but if the split skill's output is not an array, the iteration fails. Actually, the split skill outputs 'textItems' as an array, but the target name is 'pages', so the output is at '/document/pages', which is an array. The context '/document/pages/*' should work. However, the issue is that the split skill's output is 'textItems' but the target name is 'pages', so the actual output node is '/document/pages'. The entity recognition skill inputs source '/document/pages/*' expects each page's content, but the input field name is 'text' and source is correct. Another potential issue is that the split skill's output is not being passed correctly because the entity recognition skill is not referencing the correct output from the split skill. Actually, the correct output should be '/document/pages' which is an array of strings. The entity recognition skill context '/document/pages/*' should iterate over each page. But the entity recognition skill's input source is '/document/pages/*', which is incorrect because that would be the element itself, not the text content. The input source should be '/document/pages/*' to get the text of each page. However, the split skill outputs the text items as strings, so '/document/pages/*' would be the string content. That should work. Wait, the exhibit shows the split skill output target name 'pages', so the output node is '/document/pages' (array). The entity recognition skill input source is '/document/pages/*', which is the individual page string. That seems correct. However, the entity recognition skill expects a 'text' input, and the source is '/document/pages/*' which is the page text. So why would it not execute? Possibly because the language code source '/document/language' is not present in the document. But that would cause an error for the split skill too. Another reason could be that the entity recognition skill requires the language code to be provided, and if it's missing, the skill fails. But the question says 'not executing on any document', implying it never runs. The most likely cause is that the split skill is not producing pages because the maximumPageLength might be too large and the content is short, but that would still produce one page. Actually, the split skill will always produce at least one page. The exhibit shows the skillset, but the entity recognition skill context is '/document/pages/*' which is correct. However, the entity recognition skill might fail if the language code is invalid. But the most common mistake is that the entity recognition skill's context is set to '/document/pages/*', but the input source is '/document/pages/*', which is the same as context, leading to no iteration. Actually, the context defines the iteration, and the input source should be the property of that context, not the context itself. For example, if context is '/document/pages/*', then input source should be relative to that context, like 'text' if the page object had a 'text' property. But here, the input source is '/document/pages/*' which is an absolute path that points to the same node as the context, so it might cause a conflict. In Azure AI Search, the input source should be a path relative to the context or absolute. If the source is absolute and points to the same node as the context, it might not work as expected because the skill expects the input to be a scalar value, but the context is an array element. This is a known issue. Option C states that the input source path should be relative to the context, not absolute. That is the correct answer.
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.
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?
Static NAT maps one inside address to one outside address.
About these practice questions
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These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You have an Azure AI Search skillset defined as shown in the exhibit. When you run the indexer, the enrichment pipeline produces outputs but no entities are extracted. The source documents are in English and contain clear organization and person names. What is the most likely cause?
medium
A.The skill output is not mapped to the index.
B.The skills are in the wrong order.
C.The documents are not in English.
✓ D.The '/document/content' field is an array, but the skill expects a string.
Why D: The default version of EntityRecognitionSkill (V3) requires the 'text' input to be a string, not an array. If '/document/content' is an array (e.g., from OCR output), the skill fails silently. Option A is wrong because the language is correct. Option B is wrong because the skill outputs are configured. Option D is wrong because the skill is in the correct order (no dependency).
Variation 2. You are reviewing the skillset definition for an Azure AI Search indexer. The SplitSkill splits the document content into pages of 5000 characters. The SentimentSkill is set to run on each page. However, the sentiment analysis is not producing correct results. What is the most likely cause?
hard
A.The maximumPageLength of 5000 is too high for sentiment analysis
✓ B.The input source for SentimentSkill should be '/document/pages/*' but the SplitSkill output is named 'pages', so the input should be '/document/pages'
C.The context of the SentimentSkill is set to an array, which is not supported
D.The SentimentSkill uses an incorrect @odata.type version
Why B: Option C is correct because the SentimentSkill context is '/document/pages/*', which iterates over each page, but the input source is also '/document/pages/*', which should be the text from each page. However, the issue is that the SentimentSkill expects a single string, but the SplitSkill outputs an array. The input should be '/document/pages/*' but that is an array; the correct mapping would be to use the split output correctly. Actually, the error is that the input source should be '/document/pages/*' which is the array item, but the sentiment skill might be receiving an array instead of a string if not properly configured. Option C identifies that the input source should be the textItems from the split output. Option A is wrong because the skill version is fine. Option B is wrong because context can be arrays. Option D is wrong because the maximumPageLength is valid.
Last reviewed: Jun 20, 2026
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