AI-102 Implement knowledge mining and document intelligence solutions — Timed 10 Questions

Question 1

A company uses Azure Document Intelligence to extract data from invoices. They deploy the model to a container for on-premises processing. After deployment, they notice that the container consumes more memory than expected. What should they do to optimize memory usage?

10 minute time limit — choose an answer to begin.

Accepted Answer

Set the 'Memory' environment variable to a lower value in the container configuration. Azure Document Intelligence containers expose a 'Memory' environment variable that allows you to limit the container's memory allocation. By setting this variable to a lower value, you constrain the container's memory usage, which directly addresses the issue of higher-than-expected consumption. This is the recommended approach for optimizing memory in containerized deployments.

Answer

Use the 'Read' model instead of the 'Layout' model

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Use the cloud API instead of the container

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Reduce the batch size in the client application

Question 2

A company uses Azure Document Intelligence to extract data from tax forms. They need to improve accuracy for a specific field. Which TWO actions should they take?

Accepted Answer

Label more examples of the specific field in the training set. Labeling more examples of the specific field in the training set directly provides the custom model with additional ground-truth annotations for that field. This increases the model's ability to learn the variations in handwriting, formatting, and layout for that field, which is the most effective way to improve extraction accuracy for a targeted field in Azure Document Intelligence custom models.

Answer

Increase the batch size in the analysis request

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Reduce the image resolution to 200 DPI

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Use the prebuilt-tax.us model

Question 3

A company uses this skillset in an Azure AI Search enrichment pipeline. They notice that the enrichment pipeline fails when processing a document larger than 5000 characters. What is the most likely cause?

Accepted Answer

The output field mapping is missing or incorrect. The enrichment pipeline fails because the output field mapping is missing or incorrect. When a skillset processes documents, the output of each skill must be explicitly mapped to an index field; if this mapping is absent or misconfigured, the pipeline cannot store the enriched data and fails, especially for larger documents that produce more output data.

Answer

The maximum page length is too small

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The default language code is not supported

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The text split mode should be 'sentences'

Question 4

A company uses Azure Cognitive Search to index customer support emails. They need to implement a custom skill that extracts the sentiment of the email body and also identifies the primary product mentioned. The custom skill is a Python function deployed as an Azure Function. They want to ensure the skill can process multiple documents concurrently and handle errors gracefully. Which THREE configurations should they apply?

Accepted Answer

Set the inputs of the skill to include the 'text' field from the enriched document and the 'product' field from a previous skill.. Option B is correct because a custom skill's inputs must map the enriched document fields the Python function needs — here the 'text' field for sentiment analysis and the 'product' field produced by a previous skill — via the inputs array with name/source mappings. Option C is correct because a custom skill is invoked as an HTTP endpoint, so the uri must point to the Azure Function's URL and include the function key (e.g., ?code=...) for authentication, since Cognitive Search calls the function over HTTP. Option E is correct because the batchSize property on the custom skill controls how many documents are sent to the function in a single call, enabling concurrent/parallel processing of multiple documents. Option A is wrong because continueOnError should be set to true (not false) to handle errors gracefully and let the indexer continue when a single document fails. Option D is wrong because '/document/pages/*' context is used for splitting documents into pages (as with the Document Extraction skill), not for a custom skill that processes whole email bodies, and it would not achieve the required concurrency or error handling.

Answer

Set continueOnError to false to ensure that if the skill fails for one document, the entire indexer run stops.

Answer

Set the context property to '/document/pages/*' to process each page of the email individually.

Question 5

Refer to the exhibit. You have a skillset with two skills. You run the indexer and find that the output field 'organizations' is empty for documents that clearly contain organization names. The 'keyPhrases' output is populated correctly. What is the most likely cause of the issue?

Accepted Answer

The skill is not configured with a 'defaultLanguageCode' and the documents are not in English.. The 'keyPhrases' output is populated correctly, indicating the text extraction and overall pipeline are functional. The Entity Recognition skill requires a 'defaultLanguageCode' to correctly identify entities; if it is not set and the documents are not in English, the skill may fail to extract organizations, resulting in an empty 'organizations' field. This is a known behavior where the skill defaults to English and cannot process other languages without explicit configuration.

Answer

The skill's 'name' property is set to '#1', which is invalid.

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The 'categories' property is misspelled; it should be 'entityCategories'.

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The input source '/document/content' is incorrect; it should be '/document/text'.

Question 6

You are a developer for a legal firm. The firm uses Azure Cognitive Search to index legal documents. They have a custom skill that performs OCR on scanned PDFs using Azure Form Recognizer. The skill is implemented as an Azure Function. Recently, the indexer has been failing with the error: "The request was canceled due to the configured HttpClient.Timeout of 100 seconds elapsing." The documents are large (up to 200 pages each). The skill calls the Form Recognizer API asynchronously. You need to resolve the timeout issue without losing the ability to process large documents. Current configuration: batchSize = 1, maxPageSize = 100, timeout = 100 seconds. You cannot change the execution time of the Form Recognizer API. What should you do?

Accepted Answer

Decrease the batchSize to 1 and increase the timeout property in the skillset skill definition to PT300S.. The error indicates the HttpClient timeout (100 seconds) is too short for large documents processed by the custom skill. Increasing the timeout to PT300S (300 seconds) in the skill definition allows the Azure Function to wait longer for the asynchronous Form Recognizer API to complete, while keeping batchSize=1 ensures each invocation handles one document at a time, preventing overload and maintaining reliability for large PDFs.

Answer

Increase the batchSize to 10 to process more documents per invocation, reducing the number of total invocations.

Answer

Increase the maxPageSize to 500 to reduce the number of API calls to Form Recognizer.

Answer

Remove the custom skill and use the built-in OCR skill in the skillset.

Question 7

A logistics company uses Azure AI Search with an indexer and skillset to enrich shipping manifests. The skillset includes a custom skill hosted on an Azure Function that enriches each document. During a full reindex, the indexer reports that the custom skill returns HTTP 429 responses for many documents, and enriched fields are missing. You need to make the pipeline resilient without changing the Function's logic. What should you do?

Accepted Answer

Reduce the indexer batch size and configure the custom skill to handle throttling with retry and backoff.. The custom skill is being throttled, so the pipeline must reduce request pressure and retry transient failures. Decreasing the indexer batch size lowers concurrent invocations, and adding retry with backoff inside the custom skill handles HTTP 429 responses gracefully. Simply raising failure thresholds or adjusting batch sizes without retry does not recover the missing enrichment outputs.

Answer

Configure the custom skill with a batchSize and set the indexer's maxFailedItems and maxFailedItemsPerBatch to allow retries.

Answer

Add a custom skill definition with a batchSize and increase the indexer's maxFailedItemsPerBatch.

Answer

Increase the indexer batch size and set maxFailedItems to a higher value.

Question 8

You are building a knowledge mining solution with Azure AI Search. Documents are stored in Azure Blob Storage and include scanned PDFs with embedded images and text. You need to extract both the text and the textual content of images during indexing. You create a data source, an index, and an indexer. You must configure the indexer to use the built-in OCR skill. What should you do first?

Accepted Answer

Attach a cognitive services account to the skillset and enable image extraction in the indexer configuration.. The built-in OCR skill in Azure AI Search requires normalized images as input. To generate these, the indexer must be configured with imageAction set to generateNormalizedImages (or a similar option) and the skillset must be attached to a cognitive services account for billing. This allows the OCR skill to extract text from images during indexing.

Answer

Configure the indexer to use the delimitedText parsing mode and set the first line as headers.

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Add a custom skill that calls the Azure AI Vision Read API directly and set the batch size to 1.

Answer

Create a separate indexer for images and use a file projection to store the extracted text.

Question 9

You are building a knowledge mining solution in Azure AI Search. Documents are ingested from Azure Blob Storage and must be enriched with a custom skill that calls an Azure Function. The Function requires an API key that must not be stored in the skillset definition or source code. The indexer runs on a schedule. You need to configure the custom skill so that the API key is supplied securely at runtime. What should you do?

Accepted Answer

Create an Azure Key Vault managed HSM and assign the Search service managed identity the Key Vault Secrets User role.. The secure approach is to let the indexer authenticate to the Azure Function by using a managed identity and to retrieve the required key from Key Vault at runtime. This avoids embedding secrets in the skillset or indexer definition and satisfies the rotation and audit requirements of a production knowledge mining pipeline.

Answer

Define the API key in the indexer's configuration object and reference it from the custom skill by using a skillset variable.

Answer

Add the API key as a base64-encoded value in the custom skill's batch size property and decode it in the Azure Function.

Answer

Store the API key in Azure Key Vault and reference the secret by using a key vault secret URI in the custom skill's HTTP headers.

Question 10

A financial services firm uses an Azure AI Search indexer with a custom WebApiSkill that calls an internal scoring API. The API occasionally returns HTTP 500 responses due to transient load. The indexer currently fails the entire run whenever the custom skill returns an error. You need to make the pipeline more resilient to these transient failures. What should you do?

Accepted Answer

Configure the custom skill's errorHandling property to ignore errors.. The WebApiSkill includes an errorHandling property that determines whether an error returned by the external endpoint fails the indexer or is recorded and ignored. Setting it to ignore allows the run to complete despite intermittent failures from the scoring API. Batch size, failed item thresholds, and caching affect performance or tolerance but not how skill-level errors are treated.

Answer

Enable the indexer's cache and rerun the indexer.

Answer

Set the batchSize property of the indexer to 1.

Answer

Increase the indexer maxFailedItems value to a large number.