AI-900 Practice Test 48 — 15 Questions

Question 1

A data scientist trains a model on historical data and achieves high accuracy on both the training set and a held-out test set. However, when the model is deployed in production, it performs poorly on new, unseen data. Which issue is most likely the cause?

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

Accepted Answer

Data leakage. Data leakage occurs when information from outside the training dataset is inadvertently used to train the model, causing it to learn patterns that do not generalize to new data. In this scenario, the high accuracy on both training and test sets but poor production performance indicates that the test set was contaminated with information from the future or from the target variable, making the model appear accurate during validation but fail in real-world deployment.

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Overfitting

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Underfitting

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Concept drift

Question 2

A research team wants to automatically analyze thousands of online news articles to identify the main topics discussed in each article (e.g., 'technology', 'politics', 'sports'). They need a prebuilt Azure AI Language feature that returns a list of key concepts or topics without any custom training. Which feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it automatically identifies and returns a list of the main topics, concepts, or themes discussed in a document without requiring any custom training or labeled data. This prebuilt capability is designed specifically for extracting high-level topics from unstructured text, making it ideal for analyzing thousands of news articles to determine subjects like 'technology', 'politics', or 'sports'.

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Named Entity Recognition (NER)

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Sentiment analysis

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Language detection

Question 3

What is the Azure AI Language 'orchestration workflow' feature used for?

Accepted Answer

Routing user utterances to the appropriate CLU or QnA component based on intent. The orchestration workflow feature in Azure AI Language is designed to connect multiple Conversational Language Understanding (CLU) and Question Answering (QnA) projects into a single endpoint. It uses a top-level orchestrator model to classify the user's intent and then routes the utterance to the appropriate child project (CLU or QnA) for further processing, enabling a unified conversational experience across different knowledge bases.

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Automating data processing pipelines in Azure Data Factory

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Scheduling NLP model training jobs at regular intervals

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Translating utterances between languages before processing

Question 4

What does Azure AI Vision's 'dense captioning' feature do?

Accepted Answer

Generates natural language descriptions for multiple regions within a single image. Azure AI Vision's dense captioning feature goes beyond generating a single caption for the entire image. It analyzes the image to identify multiple distinct regions (e.g., a person, a car, a building) and generates a natural language description for each region, along with bounding box coordinates. This is correct because the feature's core purpose is to provide granular, region-level descriptions, not just a single long caption.

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Creates very long detailed captions for entire images

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Extracts text from dense text-heavy images like documents

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Analyzes the density of objects in an image for crowd counting

Question 5

What is a 'compute instance' in Azure Machine Learning?

Accepted Answer

A managed cloud workstation for interactive ML development with pre-installed tools. A compute instance in Azure Machine Learning is a fully managed cloud workstation that provides a pre-configured environment with popular ML tools like Jupyter Notebooks, TensorFlow, and PyTorch. It is designed for interactive development, allowing data scientists to train and experiment with models without managing infrastructure.

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A scalable cluster for running distributed training jobs across many nodes

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A virtual machine that automatically scales to run batch predictions

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A serverless execution environment for ML inference requests

Question 6

A parking management company uses cameras at the entrance and exit of a lot. They need to automatically read the license plate numbers of each car as it enters and exits. Which Azure Computer Vision capability is specifically designed for this task?

Accepted Answer

Optical Character Recognition (OCR). Optical Character Recognition (OCR) is the Azure Computer Vision capability specifically designed to extract printed or handwritten text from images, including license plate numbers. In this scenario, the cameras capture images of cars entering and exiting, and OCR processes those images to read the alphanumeric characters on the license plates. This is the exact use case for OCR, as it can handle varied fonts, angles, and lighting conditions common in parking lot environments.

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Object detection

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Image classification

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Facial recognition

Question 7

What is the difference between zero-shot, one-shot, and few-shot learning in prompting?

Accepted Answer

Zero-shot uses no examples; few-shot provides multiple examples in the prompt to guide responses. Zero-shot learning involves providing no examples in the prompt, relying solely on the model's pre-trained knowledge to generate a response, while few-shot learning includes multiple examples (typically 2–5) within the prompt to guide the model's output pattern. This distinction is fundamental to prompt engineering in generative AI workloads on Azure, where the number of examples directly influences output consistency and task specificity without retraining the model.

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They refer to how many GPUs are used for model training

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They refer to how many training epochs the model underwent

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Zero-shot is for beginners; few-shot is for experts

Question 8

A developer uses Azure OpenAI to generate Python code snippets. They want to prevent the model from producing overly long and complex functions by setting a maximum length for the generated output. Which parameter should the developer set in the API call?

Accepted Answer

max_tokens. The `max_tokens` parameter controls the maximum number of tokens (words or subwords) the model can generate in a single response. By setting a lower `max_tokens` value, the developer caps the length of the generated Python code, preventing overly long and complex functions. This directly addresses the requirement to limit output length.

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temperature

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top_p

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frequency_penalty

Question 9

A software company uses Azure OpenAI to generate code snippets. They want to evaluate how confident the model is in each token it generates. Which Azure OpenAI feature provides a numerical measure of confidence for each generated token?

Accepted Answer

Logprobs. Logprobs (log probabilities) is the Azure OpenAI feature that provides a numerical measure of confidence for each generated token. It outputs the log probability of each token being selected by the model, allowing developers to assess how certain the model is about its predictions at the token level.

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Temperature

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Top-p

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Presence penalty

Question 10

A company wants to use Azure OpenAI Service to generate product descriptions. They need to ensure the model's output is based on their specific product catalog and pricing, not on generic information. Which approach should they use?

Accepted Answer

Implementing Retrieval Augmented Generation (RAG) with their catalog.. Retrieval Augmented Generation (RAG) allows the model to dynamically retrieve relevant product catalog and pricing information from an external knowledge base at inference time, ensuring the generated descriptions are grounded in the company's specific data rather than relying on the model's generic training data. This approach avoids the need for costly fine-tuning and keeps the output up-to-date without retraining.

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Fine-tuning the model on their product catalog.

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Using few-shot learning with examples.

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Increasing the temperature parameter.

Question 11

A data scientist trains a model to predict house prices using features like number of bedrooms, square footage, and location. The model achieves a mean absolute error (MAE) of $5,000 on the training data but $25,000 on the test data. Which problem is the model most likely experiencing?

Accepted Answer

Overfitting. The model performs well on training data (MAE $5,000) but poorly on test data (MAE $25,000), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data too well, failing to generalize to unseen data. In Azure Machine Learning, this can be detected by comparing training vs. validation metrics and is often mitigated using regularization techniques or simplifying the model.

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Underfitting

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Multicollinearity

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Class imbalance

Question 12

A security company needs to monitor a warehouse using video cameras. They want to detect whether any persons are present in a given frame and also know their approximate locations. Which Azure Computer Vision capability should they use?

Accepted Answer

Object detection. Object detection is the correct choice because it not only identifies whether persons are present in a video frame but also provides bounding box coordinates indicating their approximate locations. This capability is specifically designed to locate multiple objects of interest within an image, which directly matches the requirement of detecting persons and knowing where they are.

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Image classification

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Semantic segmentation

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Optical Character Recognition (OCR)

Question 13

A customer support team wants to automatically extract the most important words or short phrases from each customer service ticket to understand common issues. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it is specifically designed to identify and return the most important words and short phrases from a document, such as a customer service ticket. This allows the support team to automatically surface common issues by analyzing the extracted key phrases across many tickets. The other options serve different purposes: named entity recognition identifies specific entities like people or organizations, sentiment analysis detects emotional tone, and language detection identifies the language of the text.

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Named entity recognition

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Sentiment analysis

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Language detection

Question 14

A data scientist trains a model to predict the exact number of cars that will cross a bridge each day for maintenance planning. The model uses historical traffic data as input. Which type of machine learning task is this?

Accepted Answer

Regression. The model predicts a continuous numerical value (the exact number of cars) based on historical traffic data. Regression is the correct machine learning task for predicting continuous numeric outcomes, such as counts, prices, or temperatures, making option B correct.

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Classification

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Clustering

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Reinforcement learning

Question 15

A consumer electronics company collects online reviews about their latest smartphone. They want to identify specific aspects that customers praise or criticize, such as battery life, camera quality, and screen brightness. Which Azure AI Language feature should they use to extract these aspect-based opinions?

Accepted Answer

Sentiment analysis with opinion mining. Sentiment analysis with opinion mining is specifically designed to extract aspect-based opinions from text. In this scenario, the company needs to identify which aspects (e.g., battery life, camera quality) are praised or criticized, which requires both aspect detection and sentiment polarity assignment. Opinion mining extends standard sentiment analysis by linking sentiments to specific targets or aspects within the text.

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Key phrase extraction

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Named entity recognition

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Language detection