AIF-C01 Practice Test 25 — 15 Questions

Question 1

A research team is using Amazon SageMaker to fine-tune a large language model. They want to optimize training cost and time without sacrificing model quality. Which THREE strategies should they implement? (Choose 3)

Accepted Answer

Apply parameter-efficient fine-tuning (PEFT) techniques like LoRA.. Option B is correct because Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) freeze the pre-trained model weights and inject trainable rank decomposition matrices into specific layers. This drastically reduces the number of trainable parameters (often by 10,000x), lowering memory and compute requirements while preserving model quality, making it ideal for cost- and time-sensitive fine-tuning.

Answer

Use a larger instance type with more GPUs.

Answer

Increase the batch size to the maximum that fits in GPU memory.

Question 2

A company is deploying a generative AI model on Amazon Bedrock and needs to monitor for potential misuse. Which THREE measures should they implement? (Choose 3)

Accepted Answer

Configure Amazon Bedrock Guardrails to block harmful content.. Option B is correct because Amazon Bedrock Guardrails provides configurable content filters that can block harmful or undesirable content in both input prompts and model responses. This is a direct monitoring and prevention mechanism for misuse, allowing administrators to define policies for topics, toxicity, and sensitive information.

Answer

Require multi-factor authentication (MFA) for all API calls.

Answer

Place the Bedrock endpoint in a private VPC with no internet access.

Question 3

A developer attached this IAM policy to a role used by an application that invokes Claude v2 in us-east-1. The application receives an access denied error. What is the MOST likely cause?

Accepted Answer

The Deny statement is blocking requests because the condition does not match the resource ARN's region. The Deny statement uses a `StringNotEquals` condition on `aws:RequestedRegion` set to `us-east-1`. This means the Deny applies to any request where the requested region is NOT `us-east-1`. Since the resource ARN in the Deny statement is `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`, the condition does not match the resource's region (the resource ARN itself is in us-east-1), but the Deny is triggered when the request is made to a different region, blocking the call. The application is likely invoking the model from a region other than us-east-1, causing the Deny to take effect.

Answer

The Allow statement does not include a condition on the region

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The Deny statement uses StringNotEquals instead of StringEquals

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The resource ARN in the Allow statement is incorrect

Question 4

A developer wants to quickly experiment with multiple foundation models using a single API. Which service provides this capability?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides a single API to access and experiment with multiple foundation models from leading AI providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon itself. This allows developers to quickly test different models without managing underlying infrastructure or learning separate APIs for each provider.

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AWS Lambda

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Amazon Bedrock

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Amazon SageMaker Studio

Question 5

A company wants to use a foundation model to automatically summarize lengthy documents. Which capability of foundation models is being utilized?

Accepted Answer

Text generation. Summarization is a text generation task where the model produces a concise version of the original content. Foundation models (e.g., GPT, Claude) are pre-trained on vast corpora and can generate coherent summaries by predicting the next tokens conditioned on the input document. This directly utilizes the text generation capability, not classification or translation.

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Sentiment analysis

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Text classification

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Machine translation

Question 6

Refer to the exhibit. A data scientist is training a neural network model on SageMaker. The training log shows the loss values per epoch. Which issue is most likely occurring?

Accepted Answer

The model is overfitting. The training log shows loss values decreasing on the training set but increasing or plateauing on the validation set, which is a classic sign 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 SageMaker, monitoring both training and validation loss curves is critical to detect this issue early.

Answer

The number of epochs is insufficient

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The dataset is too small

Answer

The learning rate is too low

Question 7

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. Option D (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.

Answer

Accuracy

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R-squared

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Precision

Question 8

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 9

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.. Option B is correct because 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.

Answer

Reduce the number of training epochs.

Question 10

Which TWO of the following are best practices for preparing training data for a machine learning model?

Accepted Answer

Handle missing values by imputing or removing them.. Option A is correct because handling missing values is a critical data preprocessing step. Missing data can introduce bias or cause algorithms to fail. Imputation (e.g., using mean, median, or model-based methods) or removal of rows/columns with missing values ensures the dataset is complete and suitable for training, preventing errors during model fitting.

Answer

Remove all outliers to improve model robustness.

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Use the entire dataset for training to maximize data usage.

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Avoid shuffling the data to preserve original order.

Question 11

A company uses a foundation model for real-time translation in a chat application. The latency is high. Which optimization would reduce latency the most?

Accepted Answer

Use model distillation to create a smaller model. Model distillation reduces the size of the foundation model by training a smaller 'student' model to mimic the behavior of a larger 'teacher' model. This directly decreases inference latency because the smaller model requires fewer computational resources (FLOPs) per forward pass, which is critical for real-time translation in a chat application where low latency is paramount.

Answer

Increase batch size

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Use a larger model

Answer

Use a CDN for model weights

Question 12

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 13

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.p3.2xlarge. Option C (ml.p3.2xlarge) is correct because it provides a GPU (NVIDIA Tesla V100) necessary for low-latency GPU acceleration in computer vision inference, while being the most cost-effective GPU instance among the options. The ml.p3.2xlarge offers a single GPU with sufficient compute for real-time inference without over-provisioning resources, minimizing cost compared to larger GPU instances like ml.p4d.24xlarge.

Answer

ml.g5.xlarge

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ml.c5.xlarge

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ml.p4d.24xlarge

Question 14

A machine learning engineer is using Amazon SageMaker to train a model and wants to automatically stop the training job if the loss does not improve for 10 consecutive epochs. Which SageMaker feature should be used?

Accepted Answer

SageMaker built-in algorithms with early stopping. Amazon SageMaker built-in algorithms support early stopping, which allows you to automatically terminate a training job when a specified metric, such as loss, stops improving for a defined number of consecutive epochs. This feature is configured directly in the algorithm's hyperparameters (e.g., `early_stopping_patience` for the XGBoost algorithm) and helps save compute time and cost by preventing overfitting.

Answer

SageMaker Training Compiler

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SageMaker Debugger

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SageMaker Experiments

Question 15

A company is using Amazon SageMaker JumpStart to deploy a pre-trained text generation model. After deployment, the model produces slow inference responses. Which action is most likely to improve inference latency?

Accepted Answer

Deploy the model on a more powerful instance type with higher GPU memory.. Option B is correct because deploying the model on a more powerful instance type with higher GPU memory directly addresses the computational bottleneck causing slow inference. A larger GPU provides more CUDA cores and memory bandwidth, enabling faster matrix operations and reducing the time per forward pass for the pre-trained text generation model.

Answer

Quantize the model weights to FP16 or INT8.

Answer

Fine-tune the model on a smaller dataset.

Answer

Increase the batch size for inference requests.