AIF-C01 Fundamentals of AI and ML Practice Test 6 — 15 Questions

Question 1

A company wants to deploy a real-time inference endpoint for a custom model on SageMaker. The model has high latency (100ms) and they need to handle variable traffic with spikes. Which deployment strategy is most cost-effective?

Accepted Answer

Deploy on a single SageMaker endpoint with automatic scaling. Option C is correct because a single SageMaker endpoint with automatic scaling allows the endpoint to dynamically adjust the number of instances based on traffic patterns, handling variable traffic and spikes cost-effectively. For a model with 100ms latency, automatic scaling can add instances during spikes and remove them during low traffic, ensuring you only pay for the compute resources you use while maintaining low inference latency.

Answer

Deploy on a SageMaker multi-model endpoint

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Use batch transform

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Use a single large instance type

Question 2

A developer needs to preprocess a dataset consisting of customer reviews for sentiment analysis. Which text preprocessing technique is most likely to improve model accuracy?

Accepted Answer

All of the above. Option B is correct because all three listed techniques—stemming, removing stop words, and lowercasing—are standard text preprocessing steps that collectively improve model accuracy for sentiment analysis. Stemming reduces words to root forms to consolidate similar meanings, removing stop words eliminates noise from high-frequency but low-information tokens, and lowercasing normalizes case variations. Together, they reduce the feature space and help the model focus on sentiment-bearing terms, leading to better generalization and accuracy.

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Stemming

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Removing stop words

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Lowercasing

Question 3

A company wants to build a model to forecast monthly sales. The data is a time series with trend and seasonality. Which SageMaker algorithm is most appropriate?

Accepted Answer

DeepAR. DeepAR is the most appropriate algorithm because it is specifically designed for time series forecasting, handling both trend and seasonality through autoregressive recurrent neural networks. It learns from multiple related time series and produces probabilistic forecasts, making it ideal for monthly sales prediction.

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XGBoost

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K-Means

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Linear Learner

Question 4

An organization wants to use Amazon Rekognition to analyze images of people for a security application. They must comply with GDPR. What is the best practice?

Accepted Answer

Ensure all images are anonymized before analysis. Option C is correct because GDPR requires that personal data, including facial images, be processed lawfully and with appropriate safeguards. Anonymizing images before analysis with Amazon Rekognition ensures that the data cannot be linked back to an identifiable person, thereby reducing GDPR compliance risk. This aligns with the principle of data minimization and privacy by design.

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Store images indefinitely for audit

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Use celebrity recognition

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Use face detection only

Question 5

In a binary classification problem, the model predicts majority class for all inputs. What is this issue called?

Accepted Answer

High bias. When a model predicts the majority class for all inputs, it indicates that the model is too simplistic and fails to capture the underlying patterns in the data. This is a classic symptom of high bias, where the model makes strong assumptions about the data distribution, leading to systematic underperformance on the minority class. In machine learning, high bias often results from an overly simple algorithm or insufficient model capacity, causing the model to underfit the training data.

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Overfitting

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High variance

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Underfitting

Question 6

A data scientist is using SageMaker to train a model on a dataset with many features. They suspect some features are redundant. Which feature engineering technique would help?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, effectively removing redundancy while preserving most of the variance in the data. In SageMaker, PCA can be applied via the built-in PCA algorithm or as a preprocessing step in a scikit-learn container to reduce feature space and eliminate multicollinearity.

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Feature scaling

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One-hot encoding

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Polynomial features

Question 7

A SageMaker endpoint is configured with automatic scaling. The model's inference time is 50ms, and traffic increases gradually. What scaling metric should be used to add instances before latency increases?

Accepted Answer

Invocations per instance. D is correct because 'Invocations per instance' is a custom metric that directly measures the number of inference requests each instance is handling. By setting a target value for this metric, the scaling policy can proactively add instances when the per-instance request count approaches a threshold, preventing latency increases before they occur. This is the recommended approach for SageMaker endpoints with gradual traffic increases, as it anticipates demand rather than reacting to latency spikes.

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Memory utilization

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Concurrent requests

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CPU utilization

Question 8

Which TWO of the following are types of feature scaling?

Accepted Answer

Standardization. Standardization (Z-score scaling) transforms features to have a mean of 0 and a standard deviation of 1, making it a valid type of feature scaling. It is essential when using algorithms that assume normally distributed data, such as linear regression, SVM, or PCA, and it does not bound the data to a fixed range.

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One-hot encoding

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Principal Component Analysis (PCA)

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Binning

Question 9

Which THREE are SageMaker built-in algorithms suitable for regression tasks?

Accepted Answer

Linear Learner. Linear Learner is a SageMaker built-in algorithm that supports both regression and classification tasks. For regression, it models the target variable as a linear combination of input features, optimizing for metrics like mean squared error. It is suitable for regression because it directly outputs continuous values.

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K-Means

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PCA

Question 10

Which TWO are best practices for model monitoring in production on AWS?

Accepted Answer

Monitor input data drift. Option C is correct because monitoring input data drift is a best practice for detecting changes in the distribution of incoming features compared to the training data. This helps identify when the model's assumptions about the data are no longer valid, which can degrade performance. AWS services like Amazon SageMaker Model Monitor can automatically track and alert on data drift.

Answer

Disable logging to reduce latency

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Use only CPU instances

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Retrain model daily

Question 11

A company is training a large language model using Amazon SageMaker. The training job fails with the error 'OutOfMemory'. They are using a single ml.p3.2xlarge instance. The dataset is 50GB and the model is 2GB. The training script uses standard data loading. Which action should they take to resolve the issue?

Accepted Answer

Increase the instance type to ml.p3.16xlarge. The error 'OutOfMemory' indicates that the ml.p3.2xlarge instance (with 16 GB GPU memory) cannot hold both the 2 GB model and the 50 GB dataset during training. Increasing the instance type to ml.p3.16xlarge provides 64 GB GPU memory, which is sufficient to accommodate the model and dataset without memory pressure. This directly resolves the resource constraint.

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Train the model using Spot instances

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Reduce the batch size

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Use SageMaker's Pipe mode for data loading

Question 12

A deployed model on an Amazon SageMaker endpoint is experiencing high inference latency (average 500ms) during peak hours. The model is a deep neural network with 10 million parameters. The endpoint uses a single ml.c5.xlarge instance. The company wants to reduce latency to under 200ms without retraining or changing the model architecture. Which action should they take?

Accepted Answer

Use SageMaker Neo to compile and optimize the model. SageMaker Neo compiles trained models into an optimized format for the target hardware, reducing inference latency without altering the model architecture. For a deep neural network with 10 million parameters on a CPU instance, Neo applies hardware-specific optimizations like operator fusion and memory layout tuning, which can significantly lower latency. This directly addresses the requirement to reduce latency from 500ms to under 200ms without retraining or changing the model.

Answer

Enable automatic scaling to add more instances

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Switch to a GPU-based instance type like ml.p2.xlarge

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Deploy the model on a multi-model endpoint

Question 13

A social media company needs to automatically detect and flag toxic comments in multiple languages. They have a large stream of user comments and require real-time moderation. Which AWS service is best suited for this task?

Accepted Answer

Amazon Comprehend. Amazon Comprehend is the correct choice because it is a natural language processing (NLP) service that can perform real-time toxicity detection across multiple languages using its built-in content moderation and custom classification capabilities. It analyzes text streams to identify toxic comments (e.g., hate speech, threats) and integrates with AWS streaming services like Amazon Kinesis for real-time processing.

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Amazon Lex

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Amazon Rekognition

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Amazon Translate

Question 14

Which TWO of the following are examples of supervised learning tasks that can be performed using Amazon SageMaker built-in algorithms?

Accepted Answer

XGBoost. XGBoost is a supervised learning algorithm that uses gradient-boosted decision trees for regression, classification, and ranking tasks. Amazon SageMaker's built-in XGBoost algorithm is optimized for distributed training and directly supports labeled training data, making it a correct example of a supervised learning task.

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Principal Component Analysis (PCA)

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Latent Dirichlet Allocation (LDA)

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K-Means

Question 15

A data scientist at a retail company is tasked with building a model to predict customer churn. The dataset contains 100,000 records with features such as age, purchase history, customer support interactions, and a binary label indicating whether the customer churned in the past. The team needs a model that can be deployed for real-time inference with low latency. They have limited time and want to use a built-in algorithm from Amazon SageMaker that is optimized for classification tasks. Which approach should they take?

Accepted Answer

Use Amazon SageMaker XGBoost algorithm. Amazon SageMaker's built-in XGBoost algorithm is optimized for classification tasks like binary churn prediction, supports real-time inference with low latency via SageMaker endpoints, and can handle the dataset size of 100,000 records efficiently. It is a supervised learning algorithm that directly uses the binary label for training, making it the correct choice for this scenario.

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Use Amazon SageMaker PCA algorithm

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Use Amazon SageMaker K-Means algorithm

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Use Amazon SageMaker BlazingText algorithm