AI-103 Text Analysis Practice Test 4 — 15 Questions

Question 1

You are troubleshooting a custom text classification model that exhibits high training loss and low validation accuracy, indicating severe model underfitting. What adjustment should you make to improve the model?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Increase the diversity and volume of training data and ensure model training is allowed to complete fully.. Underfitting means the model has not learned the underlying patterns in the data — training loss stays high because the model cannot even fit the training set. The remedy is to give the model more signal (more diverse, higher-volume data) and to let training run to completion so it has enough iterations to converge. Lowering thresholds or shrinking data would worsen the problem, not fix it.

Answer

Lower the confidence score threshold to 0.01.

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Reduce the training dataset size by 90%.

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Switch the resource pricing tier from Standard to Free.

Question 2

You are analyzing product reviews and need to identify specific product features mentioned by users along with whether users expressed positive or negative opinions about those specific features. Which advanced feature of sentiment analysis should you enable?

Accepted Answer

Opinion mining. Opinion mining, also called aspect-based sentiment analysis, is the advanced sentiment analysis feature that identifies specific aspects (product features) mentioned in text and determines the sentiment expressed toward each one. It returns aspect-sentiment pairs, which is exactly what is needed to know whether users felt positively or negatively about particular product features. This makes opinion mining the correct choice.

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Extractive summarization

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Key phrase extraction

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Named Entity Recognition

Question 3

You are configuring a custom named entity recognition project in Language Studio. You have tagged your training documents and want to split your dataset into training and testing sets. What are the recommended split options available in Language Studio?

Accepted Answer

Automatic split by percentage or manual assignment of documents to sets.. Language Studio for custom NER provides two dataset-splitting methods: automatic split by a percentage (e.g., 80/20) that randomly assigns documents to training and testing sets, or manual assignment where you explicitly tag each document as training or testing. Both are configured directly in the project's data labeling interface.

Answer

Splitting exclusively by alternating lines within each document.

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External split configuration via an Azure DevOps pipeline YAML file.

Answer

Command-line split using Azure PowerShell scripts only.

Question 4

You are designing a customer feedback processing system. You need to extract key phrases from customer reviews and store them in an Azure Cosmos DB database. When you inspect the JSON response from the key phrase API, what data structure is returned for the key phrases?

Accepted Answer

An array of strings representing the key phrases found in the document.. The Azure AI Language key phrase extraction API returns a 'keyPhrases' field containing a JSON array of strings, where each string is a key phrase identified in the input document. This flat array structure makes it straightforward to iterate and persist phrases into Cosmos DB. It does not return weights, categories, or confidence scores for key phrases.

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A single concatenated string with semicolons separating each phrase.

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A nested dictionary mapping phrase weights to sentiment scores.

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An array of objects containing 'text', 'category', and 'confidenceScore' properties.

Question 5

You are designing a text analysis solution using Azure AI Language. Which TWO features are available as prebuilt capabilities without requiring custom model training? (Choose two.)

Accepted Answer

Language detection. Azure AI Language provides it as a prebuilt capability that identifies the language of input text using the ISO 639-1 code, requiring no training data or custom model. Option C (Sentiment analysis) is also correct because it is a prebuilt feature that returns sentiment labels (positive, negative, neutral, mixed) and confidence scores out of the box. In contrast, option A (Custom named entity recognition) and option D (Custom text classification) are custom features that require labeled training data and a trained project before use. Option E (Custom summarization models) is not a prebuilt capability; summarization is offered as prebuilt abstractive/extractive summarization, and any custom summarization model would require training, so it does not fit the 'no custom training' requirement.

Answer

Custom named entity recognition

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Custom text classification

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Custom summarization models

Question 6

You are configuring an Azure AI Language service resource in the Azure portal. Which THREE settings or blades are available for managing the resource? (Choose three.)

Accepted Answer

Keys and Endpoint. Option C, Keys and Endpoint, is correct because every Azure AI Language resource exposes a Keys and Endpoint blade in the portal where you retrieve the subscription key(s) and the REST endpoint URL used to authenticate and call the service. Option D, Pricing tier, is correct because the Azure AI Language resource is created on a pricing tier (for example Free F0 or Standard S) that determines quotas, throughput, and billing, and it can be viewed or changed in the portal. Option E, Identity, is correct because the resource supports managed identities, and the Identity blade lets you enable a system-assigned identity or assign user-assigned identities for keyless authentication to other Azure services. Option A, Virtual machine size selector, does not belong because Azure AI Language is a fully managed PaaS cognitive service and does not provision or size VMs. Option B, Container registry integration blade, does not belong because, although Azure AI Language offers Docker containers, the resource itself does not include a container registry integration blade in the portal.

Answer

Virtual machine size selector

Answer

Container registry integration blade

Question 7

You are developing an application that uses the Azure AI Language REST API for text analysis. Which TWO requirements must be met when constructing HTTP requests to the service? (Choose two.)

Accepted Answer

Set the 'Content-Type' header to 'application/json'.. Option C is correct because the Azure AI Language REST API expects request bodies in JSON format, so the 'Content-Type' header must be set to 'application/json' for the service to correctly parse the payload. Option E is correct because the service authenticates requests using the 'Ocp-Apim-Subscription-Key' header, which must contain a valid API subscription key for the request to be authorized. Option A is incorrect because the API uses JSON, not XML, for request payloads. Option B is incorrect because authentication does not use a master account password in the query string; the subscription key is passed in the header. Option D is incorrect because the service does not rely exclusively on HTTP Basic Authentication with username and password; it uses the subscription key header.

Answer

Format all request payloads as XML documents.

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Include the master account password in the request query string.

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Authenticate exclusively using HTTP Basic Authentication with username and password.

Question 8

You need to ensure data privacy and compliance when processing user feedback containing sensitive information. Which TWO types of sensitive data can Azure AI Language PII detection identify and redact? (Choose two.)

Accepted Answer

Phone numbers. Azure AI Language's PII detection feature is designed to identify and redact personal data entities, and phone numbers (option C) are a supported PII entity category, so they can be detected and masked during text processing. Email addresses (option D) are likewise a core supported PII entity in Azure AI Language, allowing them to be recognized and redacted to meet privacy and compliance requirements. Weather forecast coordinates (option A) are geographic data points, not personal identifiers recognized as PII entities by the service. Source code syntax errors (option B) relate to code analysis, not PII detection, and CPU utilization metrics (option E) are operational telemetry rather than sensitive personal information, so neither is identified or redacted by PII detection.

Answer

Weather forecast coordinates

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Source code syntax errors

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CPU utilization metrics

Question 9

You are configuring custom named entity recognition in Language Studio. Which TWO types of entity components can you configure when defining custom entities? (Choose two.)

Accepted Answer

Regex component. In Azure AI Language custom NER, when you define an entity you can add a Regex component (option B), which matches entity instances using a regular expression pattern, and a Learned component (option D), which uses labeled training examples so the model learns to recognize the entity from context. These two component types are the ones supported in the entity definition workflow in Language Studio, and they can be combined within a single entity. The other options are unrelated to custom NER entity components: quantum computation (A), neural speech synthesis (C), and optical character recognition (E) are not configurable entity component types in Language Studio.

Answer

Quantum computation component

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Neural speech synthesis component

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Optical character recognition component

Question 10

You are planning a text analytics pipeline that uses asynchronous batch operations. Which THREE text analysis tasks support asynchronous long-running batch operations in Azure AI Language? (Choose three.)

Accepted Answer

Health text analytics. Options B, C, and D are correct because Azure AI Language exposes asynchronous long-running batch operations specifically for Health text analytics (B), Extractive summarization (C), and Abstractive summarization (D); these tasks accept a batch of documents via a submit job request and return results through a polling/operation-location pattern, which is designed for large or long-running workloads. In contrast, A (Simple key phrase extraction) and E (Language detection) are synchronous, single-request operations that return results immediately and do not provide an asynchronous batch job API.

Answer

Simple key phrase extraction

Answer

Language detection

Question 11

You are exploring Azure AI Language features. Which TWO tasks can be accomplished using prebuilt named entity recognition (NER)? (Choose two.)

Accepted Answer

Extracting person names from text. Prebuilt NER in Azure AI Language is designed to identify and categorize entities in text into general types such as Person, Location, Organization, DateTime, and Quantity. Option B is correct because extracting person names from text is a core prebuilt NER capability, returning entities of the Person category. Option C is correct because identifying geographic locations maps directly to the Location (and related GPE) entity categories that prebuilt NER recognizes. Option A is not part of NER; language detection is a separate Azure AI Language feature that returns the detected language and confidence score. Option D is not NER; redacting credit card numbers is handled by PII detection/redaction, which identifies sensitive data and can mask it, rather than general named entity extraction. Option E is not NER; classifying entire documents into custom business categories is a custom text classification task that requires training a model, not the prebuilt NER model.

Answer

Detecting the written language of a document

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Redacting credit card numbers for security

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Classifying entire documents into custom business categories

Question 12

You are designing an enterprise text analysis solution with high availability and security requirements. Which THREE practices should you implement? (Choose three.)

Accepted Answer

Implement exponential backoff retry logic in client applications to handle rate limiting (429 errors).. Option B is correct because Azure AI services enforce rate limits and return HTTP 429 (Too Many Requests) responses; implementing exponential backoff retry logic lets client applications gracefully retry after the indicated delay, which is essential for high availability under load. Option C is correct because using managed identities (via Microsoft Entra ID) eliminates hardcoded subscription keys, providing secure, credential-free authentication that supports key rotation and least-privilege access. Option E is correct because private endpoints (Azure Private Link) restrict traffic to your virtual network, removing public internet exposure and meeting enterprise network security requirements. Option A is not appropriate because storing subscription keys in plain text in a source code repository exposes secrets and violates security best practices. Option D is not appropriate because disabling TLS would remove encryption in transit, degrading security and violating compliance requirements rather than improving a legitimate design goal.

Answer

Store subscription keys in plain text configuration files inside the source code repository.

Answer

Disable TLS encryption on HTTP client requests to improve API throughput.

Question 13

You are configuring text summarization using the Azure AI Language REST API. Which THREE parameters or configuration options are associated with summarization requests? (Choose three.)

Accepted Answer

maxSentenceCount parameter. Option A (maxSentenceCount parameter) is correct because the extractive summarization task in the Azure AI Language REST API accepts a maxSentenceCount value in its parameters object to limit how many sentences the summary returns. Option C (Abstractive summarization task definition) is correct because the API supports an abstractiveSummarization task kind, which generates new natural-language summary text rather than selecting existing sentences. Option E (Extractive summarization task definition) is correct because the API also supports an extractiveSummarization task kind, which ranks and returns the most salient sentences from the input document. Option B (Audio sample rate frequency) is incorrect because sample rate is a speech/audio processing setting, not a text summarization parameter. Option D (Image resolution DPI setting) is incorrect because DPI relates to image processing and has no role in the text summarization REST API.

Answer

Audio sample rate frequency

Answer

Image resolution DPI setting

Question 14

You are evaluating the performance of a custom text classification model in Language Studio. Which THREE evaluation metrics are provided to assess model quality? (Choose three.)

Accepted Answer

F1-score. In Azure AI Language custom text classification, the evaluation page in Language Studio reports standard classification quality metrics: F1-score (B), Precision (C), and Recall (E). Precision measures the proportion of predicted labels that are correct, Recall measures the proportion of actual labels that were correctly identified, and F1-score is the harmonic mean of Precision and Recall, giving a balanced single-number summary of model quality. These three metrics are computed per class and as overall/macro averages, which is exactly what Language Studio surfaces for assessing a custom text classification model. HTTP request latency (A) and CPU utilization percentage (D) are operational/infrastructure performance measures, not model-quality evaluation metrics, so they are not provided for assessing classification accuracy.

Answer

HTTP request latency in milliseconds

Answer

CPU utilization percentage

Question 15

You are preparing training data for a custom named entity recognition (NER) project in Language Studio. Which THREE guidelines should you follow to ensure high model accuracy? (Choose three.)

Accepted Answer

Ensure consistent and accurate tagging of entities across all training documents.. Option A is correct because consistent and accurate tagging is fundamental to supervised NER training: if the same entity type is labeled inconsistently across documents, the model receives contradictory signals and cannot learn reliable entity boundaries or types. Option C is correct because Azure AI Language's custom NER guidance recommends providing sufficient labeled examples per entity type (typically at least 10-15 examples per entity, and more for complex or ambiguous entities) so the model can generalize the entity pattern rather than memorize a few instances. Option D is correct because training data should reflect the real-world distribution and variety of documents the model will encounter, including different formats, domains, and contexts, which improves generalization and reduces overfitting to a narrow sample. Option B is wrong because a single training document cannot provide enough examples or variability for the model to learn robust entity patterns, and the service requires a minimum number of documents for training. Option E is wrong because tagging every word as an entity defeats the purpose of NER, which must distinguish entities from non-entity tokens; the model also needs negative examples (words not tagged as entities) to learn what is not an entity.

Answer

Use exactly one training document for the entire project.

Answer

Tag every single word in every document as an entity.