MLS-C01 Practice Test 31 — 15 Questions

Question 1

A machine learning engineer is deploying a model to an Amazon SageMaker endpoint for real-time inference. The model requires a preprocessing step that involves tokenizing text and converting it to a numerical format. To minimize latency, where should the preprocessing logic be implemented?

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

Accepted Answer

Inside the SageMaker inference container using the inference.py script. To minimize latency, it's best to include the preprocessing logic inside the inference container that serves the model. This avoids additional network calls to separate preprocessing services.

Answer

Using Amazon SageMaker batch transform

Answer

As a separate AWS Lambda function called before the endpoint

Answer

On the client side before sending the request

Question 2

A data scientist is using Amazon SageMaker to train a model. The training job fails with an error 'Insufficient instance capacity'. Which action should the scientist take to resolve this?

Accepted Answer

Retry with a different instance type or Availability Zone. Retrying with a different instance type or in a different Availability Zone often resolves capacity issues. Option A is incorrect because the error is about capacity, not IAM permissions. Option C is incorrect because increasing the number of instances may exacerbate the capacity issue. Option D is incorrect because reducing the training data size does not affect instance capacity.

Answer

Check IAM permissions

Answer

Increase the number of instances

Answer

Reduce the training data size

Question 3

A company is deploying a machine learning model using Amazon SageMaker. The model requires GPUs for inference. Which THREE configurations can the company use to meet this requirement? (Choose THREE.)

Accepted Answer

Real-time endpoints with ml.p3 instance types. Real-time endpoints (option B) support GPU instances like ml.p3. Batch Transform (option C) also supports GPU instances. Elastic Inference (option E) provides GPU acceleration without a full GPU instance. Option A (SageMaker Serverless Inference) does not support GPU. Option D (SageMaker Studio) is an IDE, not an inference option.

Answer

SageMaker Serverless Inference

Answer

SageMaker Studio

Question 4

Which THREE of the following are appropriate methods to reduce overfitting in a decision tree model?

Accepted Answer

Prune the tree after training. To reduce overfitting in a decision tree, we aim to decrease model complexity. Pruning the tree after training (C) removes branches that have little predictive power, thus simplifying the model. Setting a minimum number of samples required to split an internal node (D) prevents the tree from learning overly specific patterns from small subsets. Limiting the maximum depth of the tree (E) restricts the number of splits, reducing complexity. On the other hand, increasing the number of features considered for each split (A) can make the tree more prone to overfitting by including more irrelevant features, and increasing the maximum depth (B) allows the tree to grow deeper and capture noise, both of which increase overfitting.

Answer

Increase the number of features considered for each split

Answer

Increase the maximum depth of the tree

Question 5

Which TWO of the following are valid techniques for handling missing values in a dataset for machine learning?

Accepted Answer

Remove rows with missing values. The valid techniques for handling missing values are removing rows with missing values and replacing missing values with the mean of the feature. Options B and E are correct. Option A (maximum value) introduces bias, option C (random noise) distorts distribution, and option D (string conversion) is inappropriate for numerical data.

Answer

Replace missing values with the maximum value of the feature

Answer

Replace missing values with random noise

Answer

Convert missing values to the string 'missing'

Question 6

A data scientist is training a model using SageMaker and wants to automatically stop training when the model stops improving. Which TWO options can be used?

Accepted Answer

Built-in early stopping in XGBoost. Built-in early stopping in XGBoost (Option B) is correct because XGBoost natively supports an `early_stopping_rounds` parameter that halts training when the validation metric stops improving for a specified number of rounds. SageMaker Debugger (Option C) is correct because it can monitor training metrics in real time and trigger a stop action via a built-in or custom rule (e.g., `VanishingGradient` or `LossNotDecreasing`) when the model stops improving, integrating with SageMaker's `StopTraining` API.

Answer

AWS Step Functions

Answer

CloudWatch Alarms

Answer

SageMaker Model Monitor

Question 7

A company is training a large language model on Amazon SageMaker using a single GPU instance. The training is taking too long. Which change would most likely reduce training time?

Accepted Answer

Use a larger instance with multiple GPUs and enable distributed training. Using multiple GPUs in a distributed training job can parallelize work and reduce time. Option B is wrong because increasing instance memory may not help if the bottleneck is GPU compute. Option C is wrong because decreasing batch size often increases training time due to more updates per epoch. Option D is wrong because S3 Glacier is designed for archival storage with slower retrieval, not faster access.

Answer

Increase the instance memory

Answer

Decrease the batch size

Answer

Store the training data in S3 Glacier for faster access

Question 8

A data scientist is performing feature selection for a linear regression model. Which TWO methods are appropriate? (Choose TWO.)

Accepted Answer

Lasso (L1) regularization. Both Lasso (L1) regularization and forward selection are appropriate feature selection methods for linear regression. Lasso adds an L1 penalty that shrinks some coefficients exactly to zero, effectively selecting features. Forward selection iteratively adds features based on improvement to the model. Option B (Ridge) is incorrect because L2 regularization shrinks coefficients but does not set them to zero. Option C (t-SNE) is a nonlinear dimensionality reduction technique for visualization, not feature selection. Option E (PCA) creates new components, but does not select original features.

Answer

Ridge (L2) regularization

Answer

t-distributed stochastic neighbor embedding (t-SNE)

Answer

Principal component analysis (PCA)

Question 9

A machine learning team trains a deep learning model on SageMaker. The training job uses a single ml.p3.2xlarge instance and takes 12 hours. The team needs to reduce training time without changing the algorithm. Which approach is most effective?

Accepted Answer

Use SageMaker's distributed data parallelism across multiple instances. Using SageMaker's distributed data parallelism (e.g., with SageMaker distributed training libraries) across multiple GPUs can significantly reduce training time by splitting the mini-batches across GPUs. Increasing instance type to a single larger GPU (e.g., p3.16xlarge) helps but is less effective than multi-GPU distribution. Hyperparameter tuning doesn't directly reduce training time. Spot instances may interrupt.

Answer

Increase the learning rate

Answer

Switch to a larger instance type, such as ml.p3.16xlarge

Answer

Use managed Spot Training

Question 10

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents only 2% of the data. The model achieves 99% accuracy but only identifies 5% of actual positives. Which metric should the scientist use to evaluate the model's ability to detect the positive class?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which is the key concern here. Accuracy is misleading due to class imbalance.

Answer

Accuracy

Answer

F1-score

Answer

Precision

Question 11

A data scientist trains a linear regression model to predict house prices. The model has high bias (underfitting). Which action is most likely to reduce bias?

Accepted Answer

Increase model complexity. Increasing model complexity (e.g., adding polynomial features or using a more flexible algorithm) can reduce bias. Adding L1 regularization increases bias, reducing features reduces complexity, and lowering max_depth for a tree also increases bias.

Answer

Reduce the number of features

Answer

Decrease the maximum depth of the tree

Answer

Add L1 regularization

Question 12

A data scientist is using Amazon SageMaker to train a model. The training dataset is stored in S3 as CSV files. The scientist wants to use the SageMaker built-in Linear Learner algorithm. Which input mode should be used for optimal performance?

Accepted Answer

Pipe mode. Pipe mode streams data directly from S3 to the algorithm without writing to disk, reducing I/O overhead. File mode downloads the entire dataset to disk, which is slower. Fast file mode is not a SageMaker feature. Augmented manifest is for additional metadata, not performance.

Answer

Augmented manifest file mode

Answer

File mode

Answer

Fast file mode

Question 13

A data science team is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The team wants to maximize the F1 score. Which built-in SageMaker algorithm is most appropriate?

Accepted Answer

XGBoost. XGBoost supports scale_pos_weight to handle class imbalance, directly optimizing for F1. Linear Learner with balanced class weights can also help but typically optimizes log loss. K-Means is unsupervised. PCA is for dimensionality reduction.

Answer

Linear Learner

Answer

PCA

Answer

K-Means

Question 14

Which THREE evaluation metrics are appropriate for a multi-class classification problem? (Choose 3.)

Accepted Answer

Confusion matrix.. Confusion matrix (A) is appropriate because it provides per-class performance metrics (TP, FP, FN, TN) for each class, which is essential for multi-class evaluation. Accuracy (B) is appropriate as it measures overall correctness across all classes, a common and intuitive metric for multi-class problems. F1 score with macro or micro averaging (E) is appropriate because macro averaging computes F1 per class and averages them equally, while micro averaging aggregates contributions across all classes, both suitable for multi-class. Mean squared error (C) is incorrect; it is a regression metric not used for classification. Precision-recall curve (D) is typically used for binary classification, not standard for multi-class without extensions.

Answer

Mean squared error.

Answer

Precision-recall curve.

Question 15

A data scientist trains a neural network using TensorFlow on SageMaker. The training job fails with a 'CUDA out of memory' error. What is the most likely cause and solution?

Accepted Answer

The model is too large for the GPU. Use a smaller batch size.. CUDA out of memory indicates that the GPU memory is insufficient for the batch size or model size. Reducing the batch size is a common fix. Switching to CPU is not ideal for deep learning. Increasing the number of instances may help but requires distributed training setup. Upgrading to a larger instance type is another option, but reducing batch size is simpler.

Answer

The dataset is too large. Use SageMaker Pipe mode.

Answer

The training script has a bug. Use SageMaker Debugger.

Answer

The instance type is insufficient. Use distributed training across multiple instances.