AIF-C01 Fundamentals of AI and ML Practice Test 3 — 15 Questions

Question 1

A manufacturing company is deploying IoT sensors to monitor equipment performance. The sensors generate continuous unlabeled time-series data with thousands of dimensions. The goal is to detect anomalies indicating potential failures in real time. The data science team has experience with unsupervised learning and wants to use a SageMaker built-in algorithm that can handle high-dimensional data and identify outliers. They also need to reduce the number of dimensions to improve training speed without losing important information. Which approach should they take?

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

Accepted Answer

Use Amazon SageMaker Random Cut Forest algorithm. Amazon SageMaker Random Cut Forest (RCF) is a built-in unsupervised algorithm specifically designed for anomaly detection on high-dimensional time-series data. RCF does not explicitly reduce dimensions via techniques like PCA; instead, it randomly samples features at each split in its ensemble of random trees. This random subsampling effectively reduces the number of dimensions considered per tree, allowing the algorithm to handle thousands of dimensions without explicit reduction while preserving anomaly detection accuracy. Since the team needs to detect anomalies and the data is unlabeled, RCF is the most appropriate choice among the options. Note that no SageMaker built-in algorithm performs explicit dimensionality reduction; RCF's random feature sampling meets the requirement of improving training speed by reducing the effective feature space per tree.

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Use Amazon SageMaker Linear Learner algorithm

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Use Amazon SageMaker Image Classification algorithm

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Use Amazon SageMaker Object Detection algorithm

Question 2

Refer to the exhibit. A data scientist trained an XGBoost model using Amazon SageMaker. Which TWO actions should the data scientist take to improve the model's performance based on the exhibited training job metrics and resource configuration?

Accepted Answer

Decrease the learning rate and increase the number of training rounds.. The training metrics show the model is underfitting (high training and validation error that plateaus rather than diverging), so the data scientist should increase model capacity and give the optimizer more opportunity to converge. Option B is correct because decreasing the learning rate (for example, from 0.1 to 0.05 or lower) with a corresponding increase in num_round lets XGBoost take smaller, more careful boosting steps and fit the data more accurately without overshooting. Option C is correct because increasing max_depth (or max_leaf_nodes in the SageMaker XGBoost hyperparameters) raises the maximum number of leaf nodes per tree, increasing model complexity so the ensemble can capture more structure in the data. Option A is wrong because reducing VolumeSizeInGB only affects storage cost and has no impact on model accuracy. Option D is wrong because increasing InstanceCount to 4 enables distributed training for speed/scale, not accuracy, and SageMaker XGBoost distributed training can even degrade performance if data is split poorly. Option E is wrong because a more powerful instance type only reduces training time; it does not change the algorithm's capacity or convergence behavior.

Answer

Reduce the VolumeSizeInGB to save costs.

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Use a distributed training strategy by increasing InstanceCount to 4.

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Switch to a more powerful instance type to reduce training time.

Question 3

A company is building a chatbot to answer customer queries using Amazon Lex. The development team has created a large dataset of customer interactions and intends to use Amazon SageMaker to train a custom machine learning model for natural language understanding (NLU). The team wants to integrate the trained model with Amazon Lex to handle intents and slots. The team has limited experience with SageMaker and wants to minimize operational overhead. Which solution should the team use?

Accepted Answer

Use SageMaker Autopilot to automatically build and train the best model, then deploy to a SageMaker endpoint and use Lambda to invoke it for Lex integration.. SageMaker Autopilot automates model building, tuning, and deployment, reducing manual effort and expertise required. Option A is not supported as Amazon Lex does not directly invoke custom Docker containers; integration is typically via Lambda. Option B uses SageMaker BlazingText, which requires manual algorithm selection and tuning, not minimizing operational overhead. Option C uses Amazon Comprehend, which provides general-purpose NLP but cannot train custom NLU models for Lex intents and slots.

Answer

Encapsulate the custom model in a Docker container, push it to Amazon ECR, and create a custom machine learning resource in Amazon Lex to invoke the container directly.

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Train a custom model in SageMaker using a built-in algorithm like BlazingText, then deploy it to a SageMaker endpoint and integrate with Lex via a AWS Lambda function that calls the endpoint.

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Use Amazon Comprehend to perform sentiment analysis and entity recognition, then map the results to Lex intents using Lambda.

Question 4

A retail company wants to automatically sort incoming customer support emails into categories such as 'Billing', 'Technical', and 'General' without manually labeling any training data. Which type of machine learning is most appropriate for this task?

Accepted Answer

Unsupervised learning. Unsupervised learning is designed to find structure in unlabeled data. Clustering algorithms can group similar emails together based on their content, effectively categorizing them without any manual labeling. This directly addresses the company's need to sort emails automatically without pre-labeled examples.

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Reinforcement learning

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Transfer learning

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Supervised learning

Question 5

A data scientist is evaluating a regression model that predicts housing prices. The model's predictions are consistently off by a large margin for expensive houses but accurate for cheaper ones. Which metric would best highlight this issue?

Accepted Answer

Root Mean Squared Error (RMSE). RMSE penalizes large errors more heavily than MAE because it squares the errors before averaging. In this scenario, the model's large errors on expensive houses will significantly increase the RMSE, making it the most sensitive metric to highlight the problem. It is also in the same units as the target, aiding interpretation.

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R-squared (R²)

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Mean Absolute Error (MAE)

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Mean Squared Error (MSE)

Question 6

A retail analytics team wants to group 500,000 customers into behavioral segments using purchase frequency, average order value, and recency, but no segment labels exist. They need the algorithm to choose the number of groups itself and to flag customers whose behavior is unusual. Which combination of SageMaker built-in algorithms should they use?

Accepted Answer

K-Means for clustering and Random Cut Forest for anomaly detection. Unlabeled customer grouping is a clustering task, and K-Means is the SageMaker built-in algorithm for that, with support for tuning the number of clusters. Detecting unusual customers is an anomaly detection task, handled by Random Cut Forest, which scores each record without labels. Supervised algorithms such as Linear Learner, XGBoost, and DeepAR require labeled targets and cannot perform either unsupervised task.

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Linear Learner for regression and BlazingText for word embeddings

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XGBoost for classification and Object2Vec for embeddings

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Principal Component Analysis for dimensionality reduction and DeepAR for forecasting

Question 7

A data scientist wants to quickly build a supervised learning model for binary classification on a tabular dataset with 10,000 rows and 200 features. The dataset has some missing values and requires minimal code. Which AWS service should the data scientist use?

Accepted Answer

Amazon SageMaker Autopilot. Amazon SageMaker Autopilot is the correct choice because it automatically performs data preprocessing (including handling missing values), feature engineering, model selection, and hyperparameter tuning for supervised learning tasks like binary classification. It requires minimal code—users can simply point to a tabular dataset in Amazon S3 and specify the target column, and Autopilot will automatically train and evaluate multiple candidate models, making it ideal for quickly building a binary classifier on a 10,000-row, 200-feature dataset with missing values.

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Amazon SageMaker Studio Lab

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Amazon SageMaker Clarify

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Amazon SageMaker JumpStart

Question 8

An ML team is deploying a real-time inference endpoint for a computer vision model using Amazon SageMaker. The model requires GPU acceleration for low latency. Which instance type should the team choose to minimize cost while meeting the GPU requirement?

Accepted Answer

ml.g5.xlarge. (ml.g5.xlarge) is correct because it provides a GPU (NVIDIA A10G Tensor Core GPU) necessary for low-latency GPU acceleration in computer vision inference, while being the most cost-effective GPU instance among the options. The ml.g5.xlarge offers sufficient GPU compute for real-time inference at a lower hourly cost compared to ml.p3.2xlarge, making it the optimal choice for minimizing cost while meeting the GPU requirement.

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ml.c5.xlarge

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ml.p3.2xlarge

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ml.p4d.24xlarge

Question 9

A company needs to store large amounts of unstructured training data (images, videos) in a cost-effective manner while ensuring low-latency retrieval for training jobs running on Amazon SageMaker. Which storage solution should be used?

Accepted Answer

Amazon S3. Amazon S3 is the correct choice because it is designed for cost-effective, scalable storage of unstructured data (images, videos) and integrates natively with Amazon SageMaker for low-latency data retrieval during training jobs. S3 provides high throughput and can be accessed directly from SageMaker training instances without the need for file system mounting, making it ideal for large-scale ML workloads.

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Amazon EFS

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Amazon RDS

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Amazon EBS

Question 10

An organization wants to detect anomalies in real-time streaming data from IoT devices. The data includes sensor readings, and the team plans to use a machine learning model. Which AWS service should be used to build and deploy the model with minimal operational overhead?

Accepted Answer

Amazon SageMaker. Amazon SageMaker is the correct choice because it provides a fully managed environment for building, training, and deploying machine learning models at scale. For real-time anomaly detection on streaming IoT data, SageMaker can host a trained model as a real-time endpoint that processes incoming sensor readings via Amazon Kinesis Data Streams or AWS Lambda, minimizing operational overhead by handling infrastructure, scaling, and monitoring automatically.

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AWS Glue

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Amazon QuickSight

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Amazon Kinesis Data Analytics

Question 11

Which TWO services can be used to preprocess data for machine learning in AWS? (Choose two.)

Accepted Answer

AWS Glue. AWS Glue (A) is correct because it is a fully managed, serverless ETL service that can run Apache Spark jobs and Glue Studio/DynamicFrames to clean, transform, join, and normalize raw data into feature-ready datasets for ML training. Amazon SageMaker Data Wrangler (C) is correct because it is purpose-built for ML data preparation, providing a visual interface with 300+ built-in transforms, data quality/insight reports, and one-click export to SageMaker pipelines or feature stores. Amazon Athena (B) is a serverless interactive query service over S3 using SQL, suited for ad hoc analytics rather than building reusable preprocessing pipelines. Amazon Redshift (D) is a data warehouse for analytics and SQL workloads, not a dedicated ML preprocessing service. AWS Lambda (E) is a general-purpose serverless compute service that could run small transformation code but is not a data preprocessing service for ML.

Answer

Amazon Athena

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Amazon Redshift

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AWS Lambda

Question 12

Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)

Accepted Answer

It provides built-in workflows for image classification and object detection.. Option B is correct because Amazon SageMaker Ground Truth ships with built-in labeling task templates and workflows for common job types, including image classification and object detection (as well as text, semantic segmentation, and bounding boxes). Option C is correct because Ground Truth supports automated data labeling, which uses active learning to have a model label high-confidence data while sending only low-confidence samples to human labelers, reducing cost and effort. Option D is correct because Ground Truth integrates directly with Amazon SageMaker, writing labeled datasets to Amazon S3 in augmented manifest format that SageMaker training jobs can consume. Option A is wrong because Ground Truth handles images, video, text, and 3D point clouds, not just text. Option E is wrong because Ground Truth supports multiple workforces, including private workforces, vendor-managed workforces, and Amazon Mechanical Turk, so it is not limited to a public Mechanical Turk workforce.

Answer

It can only be used for text data.

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It can only use a public workforce from Amazon Mechanical Turk.

Question 13

A data scientist is training a binary classification model to predict customer churn. The dataset has 10,000 records with 9,500 non-churners and 500 churners. After training a logistic regression model, the model achieves 95% accuracy on the test set. However, the business team reports that the model is not useful because it predicts almost all customers as non-churners. Which metric should the data scientist use to evaluate the model's performance in this scenario?

Accepted Answer

Recall. (Recall) is correct because in this highly imbalanced dataset (95% non-churners vs 5% churners), the model's 95% accuracy is misleading—it can achieve this by simply predicting the majority class (non-churner) for all samples. Recall measures the proportion of actual churners correctly identified (True Positives / (True Positives + False Negatives)), directly addressing the business need to detect churn. A high recall ensures the model captures most churners, even at the cost of some false positives.

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Accuracy

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R-squared

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Precision

Question 14

A company wants to build a system that automatically categorizes customer support tickets into predefined categories (e.g., billing, technical, account). The team has a large dataset of historical tickets with their category labels. Which type of machine learning problem is this?

Accepted Answer

Multi-class classification. This is a multi-class classification problem because the model must assign each support ticket to one of three or more predefined categories (e.g., billing, technical, account). The dataset provides labeled historical tickets, making it a supervised learning task, and the output is a discrete class label from a set of more than two categories, which distinguishes it from binary classification.

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Regression

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Binary classification

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Clustering

Question 15

A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is taking too long. The dataset consists of 100,000 images stored in Amazon S3. Which action can the data scientist take to reduce training time without modifying the model architecture?

Accepted Answer

Use a GPU instance type for training.. GPU instances are specifically designed for parallel processing of matrix operations, which are fundamental to deep learning training. By switching to a GPU instance type (e.g., p3 or p4d families) in SageMaker, the data scientist can significantly accelerate the training of the image classification model without altering the model architecture, as the dataset of 100,000 images benefits from GPU's massive parallelism for forward and backward passes.

Answer

Convert images to CSV format before training.

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Enable checkpointing to save intermediate models.

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Reduce the number of training epochs.