AIF-C01 Fundamentals of AI and ML Practice Test 2 — 15 Questions

Question 1

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.

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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.

Question 2

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.

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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 3

A financial services company uses a machine learning model to approve loan applications. The model is a gradient boosting classifier trained on historical loan data. Recently, the company noticed that the model's approval rate for applicants from a certain demographic group is significantly lower than for other groups, even though the model's overall accuracy remains high. The data science team has been asked to address this potential bias while minimizing the impact on overall model performance. The team has access to the training data and the trained model. They have limited time and budget. Which course of action should the team take first?

Accepted Answer

Analyze the training data for bias and retrain the model using bias mitigation techniques such as reweighting.. The most efficient first step is to analyze the training data for bias and then retrain the model with bias mitigation techniques like reweighting. Option A is wrong because collecting more data is resource-intensive and may not address bias. Option C is wrong because feature engineering may not help if the bias is in the labels. Option D is wrong because post-hoc adjustments can introduce other issues and may not be as effective as addressing bias during training.

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Remove the sensitive attribute from the training data and retrain the model.

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Collect more data from the under-represented demographic group and retrain the model.

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Adjust the model's decision threshold for the affected group after deployment.

Question 4

A retail company uses a machine learning model to forecast daily product demand. The model is a time series model that uses historical sales data. The model has been performing well, but recently the forecasts have been consistently too low, leading to stockouts. The data scientist notices that the model was trained on data up to last year, and the company has since launched a successful marketing campaign that increased sales by 20%. The data scientist needs to update the model to reflect the new sales patterns. Which approach should the data scientist take?

Accepted Answer

Retrain the model on the most recent data that includes the sales from the marketing campaign.. Option D is correct because retraining the model on the most recent data that includes the sales from the marketing campaign allows the model to learn the new underlying pattern in the time series. Since the model is a time series model, it relies on historical patterns to make forecasts; retraining on data that captures the 20% sales lift ensures the model adapts to the new demand level, reducing the persistent underforecasting.

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Add a feature for the marketing campaign and continue using the old model.

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Switch to a different model type, such as ARIMA, without retraining.

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Multiply the model's predictions by 1.2 to account for the marketing campaign.

Question 5

A company wants to use AI to automatically transcribe customer service calls into text. Which AWS service is most suitable?

Accepted Answer

Amazon Transcribe. Amazon Transcribe is the correct choice because it is a fully managed automatic speech recognition (ASR) service designed specifically to convert speech into text. It can handle real-time streaming or batch processing of audio files, making it ideal for transcribing customer service calls into searchable text.

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Amazon Comprehend

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Amazon Polly

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Amazon Rekognition

Question 6

Which metric is most appropriate for evaluating a classification model when false positives are costly?

Accepted Answer

Precision. Precision is the most appropriate metric when false positives are costly because it measures the proportion of true positive predictions among all positive predictions (TP / (TP + FP)). A high precision indicates that when the model predicts a positive class, it is very likely correct, minimizing the number of false positives. This directly aligns with the business requirement to avoid costly false alarms.

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F1 score

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Recall

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Accuracy

Question 7

A data scientist is preparing data for a machine learning model. What is the purpose of splitting the data into training, validation, and test sets?

Accepted Answer

To evaluate model generalization. The test set is used to evaluate the final model's generalization performance on unseen data. The validation set is for hyperparameter tuning during development. Training set is for fitting the model.

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To tune hyperparameters

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To balance class distributions

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To prevent overfitting during training

Question 8

A company wants to automatically detect anomalies in server metrics. Which algorithm is most appropriate?

Accepted Answer

One-class SVM. One-class SVM is specifically designed for anomaly detection, as it learns a boundary around the normal data points in the feature space and identifies any point falling outside this boundary as an anomaly. This makes it ideal for detecting unusual patterns in server metrics without requiring labeled anomaly examples.

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XGBoost

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Linear SVM

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K-Means

Question 9

A team is training a binary classification model using Amazon SageMaker. They notice that the training accuracy is 99% but the test accuracy is only 70%. Which technique should they apply first to address this?

Accepted Answer

Apply regularization. The high training accuracy (99%) paired with significantly lower test accuracy (70%) is a classic symptom of overfitting, where the model memorizes the training data instead of learning generalizable patterns. Regularization (Option B) is the first-line technique to combat overfitting by adding a penalty to the loss function (e.g., L1 or L2 regularization), which discourages overly complex decision boundaries. In Amazon SageMaker, this can be implemented via hyperparameters like `l1` or `l2` in built-in algorithms or by adding dropout layers in a custom framework.

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Reduce training data

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Increase learning rate

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Increase model complexity

Question 10

During model training, the loss decreases rapidly for the first few epochs and then plateaus. The validation loss starts increasing after some epochs. What should the team do to improve generalization?

Accepted Answer

Early stopping. The validation loss increasing while training loss continues to decrease is a classic sign of overfitting. Early stopping (Option B) halts training when validation performance stops improving, preventing the model from memorizing noise in the training data and thereby improving generalization.

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Increase learning rate

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Increase training epochs

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Add more layers

Question 11

A company is deploying a machine learning model for real-time fraud detection. The model must have latency under 100ms. Which infrastructure choice is most appropriate?

Accepted Answer

Amazon SageMaker real-time endpoints. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically in the tens of milliseconds, making them suitable for real-time fraud detection where latency must be under 100ms. They deploy a model behind a persistent HTTPS endpoint that auto-scales to handle incoming requests with minimal delay.

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Amazon EC2 with Deep Learning AMI

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Amazon SageMaker batch transform

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Amazon SageMaker notebook instance

Question 12

A team trains a model using Amazon SageMaker built-in XGBoost. After training, they want to evaluate feature importance. Which SageMaker feature allows them to view this?

Accepted Answer

SageMaker Debugger. SageMaker Debugger provides built-in monitoring and visualization capabilities, including the ability to capture feature importance metrics (e.g., gain, cover, weight) from XGBoost training jobs. It automatically saves these metrics to Amazon S3 and allows you to view them through the SageMaker Studio Debugger dashboard or by querying the saved tensors, enabling direct evaluation of feature importance without additional custom code.

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SageMaker Experiments

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SageMaker Autopilot

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SageMaker Model Monitor

Question 13

An ML engineer wants to store training data in a format optimized for linear data scanning and columnar access in SageMaker. Which format is most appropriate?

Accepted Answer

Parquet. Parquet is a columnar storage format optimized for both linear data scanning and columnar access, making it ideal for training data in SageMaker. It reduces I/O by storing data by columns rather than rows, enabling efficient retrieval of specific features during model training.

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JSON

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Image (JPEG/PNG)

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CSV

Question 14

A data scientist is preparing data for a classification task. Which TWO techniques are commonly used for handling missing values? (Choose two.)

Accepted Answer

Imputing with mean. Imputing with the mean is a common technique for handling missing values in numerical features because it preserves the overall distribution of the data without reducing the dataset size. This method replaces each missing entry with the arithmetic mean of the non-missing values in that column, which is simple to implement and works well when data is missing completely at random (MCAR).

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Label encoding

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Normalization

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One-hot encoding

Question 15

Which AWS services can be used to build, train, and deploy custom machine learning models? (Choose two.)

Accepted Answer

AWS Deep Learning AMIs. AWS Deep Learning AMIs (C) are pre-configured Amazon Machine Images that include popular deep learning frameworks (TensorFlow, PyTorch, MXNet) and GPU drivers, allowing you to build, train, and deploy custom ML models on EC2 instances. Amazon SageMaker (E) is a fully managed service that provides end-to-end capabilities for building, training, and deploying custom ML models at scale, with built-in algorithms, automatic model tuning, and one-click deployment.

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Amazon Polly

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Amazon Lex

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Amazon Rekognition