AIF-C01 Fundamentals of AI and ML Practice Test 8 — 15 Questions

Question 1

A hospital wants to detect pneumonia from chest X-ray images. Radiologists have already labeled thousands of past X-rays as either 'pneumonia' or 'no pneumonia'. The hospital wants a model that generalizes to new X-rays. Which type of machine learning task is this?

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

Accepted Answer

Supervised image classification. Labeled images with known diagnoses are the defining input for supervised classification. The model learns from pneumonia and no-pneumonia examples and predicts the class for new X-rays, which is precisely the generalization the hospital wants. Unsupervised, clustering, and reinforcement approaches either ignore the labels or lack the required structure.

Answer

Reinforcement learning

Answer

Unsupervised anomaly detection

Answer

Clustering

Question 2

A retail company wants to forecast daily product demand for the next quarter. They have three years of historical sales data that includes seasonal spikes and promotional periods, and they want a fully managed AWS service that can automatically train and tune a forecasting model without writing deep learning code. Which AWS service best fits this requirement?

Accepted Answer

Amazon Forecast. The scenario calls for a managed service that learns from historical numeric sales data containing seasonality and promotions. Amazon Forecast is purpose-built for this, automatically training and tuning time-series models and exposing forecasts without custom deep learning code. The other services address computer vision, speech, and text analytics, none of which produce demand forecasts.

Answer

Amazon Comprehend

Answer

Amazon Polly

Answer

Amazon Rekognition

Question 3

A data scientist trains a model to predict whether a loan applicant will default. After deployment, the model performs well on applicants similar to the training data but poorly on applicants from a newly added geographic region that was underrepresented in training. Which statement best describes the underlying problem?

Accepted Answer

The training data does not represent the new region, so the model cannot generalize to that subpopulation.. The model fails specifically on a subpopulation that was underrepresented in training, which is a data coverage and distribution shift problem. Good performance on familiar applicants confirms the algorithm works, but without representative training examples from the new region the model cannot generalize there. The remedy is additional representative data or techniques that address distribution shift.

Answer

The model has overfit the training set because it memorized noise in the original regions.

Answer

The model requires more training epochs to converge on the new region's data.

Answer

The evaluation metric used is inappropriate and should be replaced with accuracy.

Question 4

A fintech startup is preparing its first machine learning project to detect fraudulent card transactions. The team must decide which characteristics make a problem well suited to supervised learning. Which TWO characteristics indicate that supervised learning is appropriate? (Choose two.)

Accepted Answer

Historical transactions exist that were confirmed as fraudulent or legitimate by investigators.. Supervised learning needs labeled examples and a defined target to predict. Confirmed fraudulent or legitimate transactions supply those labels, and a discrete fraud-or-not decision supplies a suitable classification target. Unlabeled data, label-free exploration goals, and explainability preferences do not establish the labeled input-output pairing that supervised learning fundamentally depends on.

Answer

The team wants the algorithm to discover hidden structure without any predefined output.

Answer

The team prefers a model that explains each prediction with feature importance values.

Answer

The dataset contains millions of unlabeled transactions with no investigator feedback.

Question 5

A hospital wants to build a model that predicts whether a patient has a specific disease based on labeled historical medical records where each record is marked either positive or negative. Which type of machine learning problem does this represent?

Accepted Answer

Supervised learning using classification. Because each historical record already includes a known positive or negative label and the target is a discrete category, the task is supervised classification. Regression would predict a continuous value, clustering ignores the labels, and reinforcement learning requires rewards from interaction. Classification directly models the disease outcome the hospital wants to predict.

Answer

Reinforcement learning using reward signals

Answer

Supervised learning using regression

Answer

Unsupervised learning using clustering

Question 6

A hospital wants to build a system that automatically assigns a specialty department (for example, Cardiology, Neurology, or Orthopedics) to each free-text patient referral note. The hospital has a large archive of past referral notes that were already labeled by clinicians with the correct department. Which type of machine learning problem does this scenario describe?

Accepted Answer

Supervised learning, because the model learns from labeled historical examples to predict a categorical outcome.. Because every historical referral note already has a clinician-assigned department, the training data is fully labeled and the target is one of several discrete categories. That combination defines supervised classification, in which a model learns the relationship between note text and department and then predicts the department for new notes.

Answer

Generative AI, because the model must create a new department name for every referral note it processes.

Answer

Unsupervised learning, because the model must discover hidden structure within the referral notes without guidance.

Answer

Reinforcement learning, because the model improves through rewards received after each department assignment.

Question 7

A media company wants to build a system that generates short promotional descriptions for its articles. The team has no labeled dataset of article-summary pairs but has a large corpus of published articles and descriptions. They want to leverage a pretrained foundation model and adapt it to their domain with minimal labeling effort. Which approach best fits this scenario?

Accepted Answer

Fine-tune a pretrained foundation model on the company's article and description corpus for text generation.. The company needs generated text and already holds a domain corpus of articles paired with descriptions. Fine-tuning a pretrained foundation model adapts broad language ability to the company's style with modest additional data, which matches the stated preference for minimal labeling. Classification, clustering, and from-scratch reinforcement learning either produce the wrong output type or discard the pretrained model advantage.

Answer

Use reinforcement learning with reader click rewards to train a description generator from scratch.

Answer

Train a supervised classification model that assigns each article to a predefined topic category.

Answer

Apply unsupervised clustering to group articles by topic and use cluster IDs as descriptions.

Question 8

A startup is preparing a dataset to train a supervised machine learning model and wants to follow sound data preparation practices. Which TWO activities are appropriate parts of preparing training data? (Choose two.)

Accepted Answer

Splitting the dataset into training, validation, and test subsets before tuning the model.. Sound preparation centers on protecting evaluation integrity and improving data quality: separating train, validation, and test subsets keeps performance estimates honest, while removing duplicates and fixing inconsistent labels prevents leakage and contradictory signals. Tuning on the test set, deleting all incomplete rows, and duplicating examples either leak information, discard useful data, or add no new information.

Answer

Adding synthetic copies of the same training examples until the dataset reaches a target size.

Answer

Tuning the model's hyperparameters repeatedly against the test set until the score improves.

Answer

Deleting all records that contain any missing values to guarantee a clean dataset.

Question 9

A media company has millions of customer support emails but no labels indicating topic or sentiment. The company wants to discover natural groupings of emails and reduce dimensionality before further analysis. Which approach should they use?

Accepted Answer

Apply unsupervised learning techniques such as clustering and dimensionality reduction. The absence of labels rules out supervised methods and points to unsupervised learning. Clustering reveals natural groupings among the emails, while dimensionality reduction techniques help compress and visualize the feature space. Together they satisfy both stated goals without requiring any annotation effort.

Answer

Use reinforcement learning to reward correct email topic assignments

Answer

Perform regression on email length to predict customer churn

Answer

Train a supervised classifier on the emails using sentiment labels

Question 10

A retail bank has millions of unlabeled customer transaction records and wants to discover natural groupings of spending behavior without defining any categories in advance. The data science team plans to use an unsupervised learning approach. Which technique is designed for this goal?

Accepted Answer

K-means clustering, which partitions records into groups so that members of a group are more similar to each other.. The bank has no predefined categories and wants to uncover structure in unlabeled data, which is the defining use case for unsupervised learning. K-means clustering groups similar records together so the team can inspect and name the resulting segments afterward, turning raw transaction behavior into actionable customer groupings.

Answer

Linear regression, which models the relationship between a continuous target and one or more input features.

Answer

Logistic regression, which estimates the probability that a record belongs to a particular class.

Answer

A decision tree classifier, which splits data using feature thresholds to predict a labeled category.

Question 11

A media company stores 40 TB of raw video footage in Amazon S3 and wants to automatically detect scene boundaries, identify on-screen text, and flag unsafe frames without building custom computer vision models. Which AWS service should they use?

Accepted Answer

Amazon Rekognition Video. Amazon Rekognition Video is purpose-built for analyzing video stored in S3 or streamed in real time, offering scene detection, text detection, content moderation, and activity recognition through managed APIs. Because the company wants those visual capabilities without training custom models, the managed video analysis service is the correct fit. The other services address speech, text, or audio generation rather than visual video understanding.

Answer

Amazon Comprehend

Answer

Amazon Polly

Answer

Amazon Transcribe

Question 12

A financial services company is preparing to train a machine learning model on customer transaction data. The data science team must address data quality concerns before training, because poor data directly harms model performance. Which TWO practices best improve the quality of the training data? (Choose two.)

Accepted Answer

Handle missing values by imputing them or removing affected records after analysis. Improving data quality before training centers on correcting defects in the data itself. Handling missing values prevents bias and instability, while scaling numeric features ensures algorithms treat inputs comparably. Hyperparameter changes, row duplication, and deleting predictive features do not repair data defects and in several cases worsen results.

Answer

Add more duplicate rows to increase the effective dataset size

Answer

Increase the learning rate so the model converges faster on noisy data

Answer

Remove all features that have any correlation with the target variable

Question 13

A media company stores thousands of hours of unlabeled video footage and wants to build a searchable index that lets editors retrieve clips by describing their content in natural language. The team has no annotated dataset and no budget to label one. Which machine learning approach is the MOST appropriate starting point?

Accepted Answer

Use a pretrained multimodal embedding model to encode video frames and text into a shared vector space, then retrieve clips by similarity.. With no labels and a need to search footage using free-form language, a pretrained multimodal embedding model is the most practical foundation. It places video and text in a shared vector space so that descriptive queries match relevant clips by similarity, enabling semantic retrieval without any custom annotation effort.

Answer

Train a supervised video classifier from scratch using the footage, treating each video file as its own class.

Answer

Apply k-means clustering to raw pixel values of every frame to produce searchable cluster identifiers.

Answer

Build a reinforcement learning agent that learns to select the clip most likely to satisfy an editor through trial and error.

Question 14

A company is building a model to detect fraudulent transactions. The dataset has 1,000,000 transactions, of which only 1,000 are fraudulent. The team wants to evaluate the model's performance. Which metric is most appropriate to use as the primary evaluation metric?

Accepted Answer

F1 score. For highly imbalanced datasets like fraud detection, accuracy is misleading. The F1 score balances precision and recall, making it suitable when both false positives and false negatives matter. It provides a single metric that reflects the model's ability to correctly identify fraud without being overwhelmed by the majority class.

Answer

Recall

Answer

Accuracy

Answer

Precision

Question 15

A financial services firm must build a model that flags potentially fraudulent card transactions in under 200 milliseconds while keeping all data inside its own Amazon VPC. The fraud team has thousands of labeled historical transactions and the pattern changes slowly over months. Which approach best balances latency, data residency, and the need for periodic retraining?

Accepted Answer

Train a supervised classification model with Amazon SageMaker, deploy it to a real-time endpoint inside the VPC, and schedule periodic retraining jobs.. A supervised classification model trained on the labeled history and deployed to a SageMaker real-time endpoint inside the VPC meets the latency and residency constraints, while scheduled retraining handles gradual fraud pattern drift. Purpose-built supervised learning uses the available labels, and in-VPC endpoints keep transaction data within the controlled network. Batch, unsupervised, or general foundation-model approaches each miss at least one hard requirement.

Answer

Deploy a pre-trained foundation model from Amazon Bedrock and prompt it to classify each transaction.

Answer

Build an unsupervised anomaly detection model on unlabeled data and run batch transform once per day.

Answer

Use Amazon Fraud Detector in evaluation mode and export predictions to an S3 bucket outside the VPC for scoring.