MLS-C01 Practice Test 32 — 15 Questions

Question 1

A data scientist is using SageMaker to train a custom TensorFlow model. The training script reads data from S3 using TensorFlow's tf.data API. The training is bottlenecked by I/O. Which strategy would MOST effectively improve data throughput?

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

Accepted Answer

Use SageMaker Pipe mode and shard the S3 dataset. Using SageMaker Pipe mode with a sharded S3 dataset allows the training instances to stream data in parallel, reducing I/O bottlenecks. Increasing workers in tf.data may help but not as effectively as optimizing data ingestion. Using FSx for Lustre provides high throughput but adds cost and complexity.

Answer

Compress the data files in S3

Answer

Use Amazon FSx for Lustre as a mounted filesystem

Answer

Increase the number of parallel workers in tf.data

Question 2

A company is building a model to classify customer reviews as positive or negative. The dataset has 10,000 positive and 100 negative reviews. Which metric is most appropriate for evaluating model performance?

Accepted Answer

F1 score.. (F1 score) is most appropriate because it balances precision and recall, making it suitable for imbalanced datasets where accuracy can be misleading (e.g., a model predicting all positive would achieve 99% accuracy). Option B (Accuracy) is misleading in imbalanced scenarios. Option C (Mean squared error) is for regression, not classification. Option D (AUC-ROC) can be used but may provide an overly optimistic assessment in highly imbalanced data.

Answer

Accuracy.

Answer

Mean squared error.

Answer

AUC-ROC.

Question 3

A data scientist is building a recommendation system for an e-commerce platform. The dataset contains user interactions (clicks, purchases) and item metadata. The scientist wants to use matrix factorization. Which algorithm should be used?

Accepted Answer

SageMaker Factorization Machines. SageMaker Factorization Machines is specifically designed for recommendation systems and matrix factorization tasks. Option A (SageMaker Image Classification) is used for image classification, not matrix factorization. Option B (SageMaker BlazingText) is used for text classification or word embeddings, not for recommendation. Option C (SageMaker XGBoost) is a gradient boosting algorithm for regression and classification, not matrix factorization.

Answer

SageMaker Image Classification

Answer

SageMaker BlazingText

Answer

SageMaker XGBoost

Question 4

A data scientist is training a deep learning model for image classification using TensorFlow on Amazon SageMaker. The model trains slowly, and the GPU utilization is below 20%. Which action will MOST effectively increase GPU utilization and reduce training time?

Accepted Answer

Increase the batch size to better saturate the GPU.. Increasing batch size provides more work per GPU step, improving utilization. Option A is wrong because reducing dataset size does not directly improve GPU utilization and may lead to underfitting. Option C is wrong because switching to CPU would be slower. Option D is wrong because decreasing batch size reduces the work per step, lowering GPU utilization and increasing training time.

Answer

Reduce the training dataset size.

Answer

Switch to a CPU-only instance.

Answer

Decrease the batch size to reduce memory pressure.

Question 5

A data scientist is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket encrypted with AWS KMS. During training, the job fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The SageMaker execution role lacks kms:Decrypt permission for the KMS key. SageMaker needs permission to use the KMS key to decrypt the data; the execution role must have kms:Decrypt permissions.

Answer

The training instance type does not support encryption

Answer

The training data is not in the same region as the SageMaker notebook

Answer

The S3 bucket policy does not allow SageMaker to list objects

Question 6

A team is training a deep learning model on Amazon SageMaker. The training job is slow because the data is stored in S3 as many small files. Which approach is MOST effective to improve training throughput?

Accepted Answer

Use SageMaker Pipe mode for training input. Using SageMaker Pipe mode streams data directly from S3, reducing startup time. Shuffling files or increasing instance count does not address the small file overhead. Using EFS would introduce latency.

Answer

Increase the number of ml.c5.xlarge instances

Answer

Shuffle the S3 objects to randomize order

Answer

Use Amazon EFS instead of S3

Question 7

A data scientist is using Amazon SageMaker to train a model with a large dataset that does not fit into memory on a single instance. The training algorithm supports distributed training. Which approach should the scientist use to train the model efficiently?

Accepted Answer

Use SageMaker Pipe mode to stream data directly from S3. SageMaker Pipe mode streams data from S3 directly to the training algorithm without writing to disk, enabling processing of large datasets beyond memory.

Answer

Use SageMaker File mode and increase the instance volume size

Answer

Use Amazon EMR to preprocess data and then train on a smaller sample

Answer

Split the data into smaller files and use multiple training jobs sequentially

Question 8

A data scientist is training a neural network on Amazon SageMaker. The network has many layers and the training is very slow. The scientist suspects that the gradients are vanishing. Which technique is most specifically designed to mitigate the vanishing gradient problem?

Accepted Answer

Use batch normalization.. Batch normalization helps by normalizing the activations, which reduces the problem of vanishing/exploding gradients. Dropout is for regularization. Data augmentation increases data. Gradient clipping deals with exploding gradients, not vanishing.

Answer

Use gradient clipping.

Answer

Use data augmentation.

Answer

Use dropout layers.

Question 9

A machine learning team is using SageMaker to train a deep learning model. The training job is failing due to insufficient GPU memory. Which approach should the team take to resolve this issue without changing the model architecture?

Accepted Answer

Use gradient accumulation to reduce the effective batch size per step.. Gradient accumulation allows the model to simulate a larger batch size without increasing memory usage per step, as gradients are accumulated over several smaller batches before updating weights. Option A is wrong because increasing the batch size would increase memory consumption per step, exacerbating the issue. Option C (adding more GPUs) is wrong because simply adding more GPUs to the instance does not reduce the memory usage per GPU; each GPU would still process the same batch size and run out of memory. Option D is wrong because decreasing the learning rate does not affect memory usage; it only changes the step size during optimization.

Answer

Increase the batch size.

Answer

Add more GPUs to the training instance.

Answer

Decrease the learning rate.

Question 10

A company is deploying a model that predicts customer churn. The model's recall for the churn class is 0.9, but precision is 0.4. The business cost of false positives is high. Which strategy would MOST likely improve precision without significantly harming recall?

Accepted Answer

Increase the decision threshold for the churn class. Adjusting the decision threshold to require a higher probability before predicting churn can reduce false positives (increase precision) but may lower recall. The goal is to find a threshold that balances both. Using more aggressive regularization or different algorithms may not directly control the trade-off.

Answer

Collect more data for the churn class

Answer

Use a different algorithm such as Random Forest

Answer

Decrease the decision threshold for the churn class

Question 11

A data scientist is using Amazon SageMaker to train a classification model. The dataset contains categorical features with high cardinality. Which encoding method is most appropriate for handling high-cardinality categorical features in a linear model?

Accepted Answer

Target encoding. One-hot encoding creates many binary columns, which can cause the curse of dimensionality for high-cardinality features. Label encoding assigns arbitrary integers, which linear models may interpret as ordinal. Target encoding (mean encoding) replaces categories with the mean of the target variable, which captures information without expanding dimensionality. This is often used for high-cardinality features. Ordinal encoding is similar to label encoding.

Answer

Label encoding

Answer

One-hot encoding

Answer

Ordinal encoding

Question 12

A team is training a large language model using SageMaker's distributed training. They notice that the training loss is not decreasing after the first few epochs. Which action is MOST likely to resolve this issue?

Accepted Answer

Reduce the learning rate. When training loss plateaus or does not decrease after the first few epochs, a common cause is that the learning rate is too high, causing the optimizer to overshoot minima. Reducing the learning rate helps the model converge. Increasing the batch size (option A) mainly affects gradient variance and training speed but does not address an overly large step size. Adding L2 regularization (option B) helps prevent overfitting but does not resolve a high learning rate. Switching from Adam to SGD (option D) may not help because Adam typically adapts learning rates per parameter; if the base learning rate is too high, both optimizers can struggle. Therefore, reducing the learning rate is the most direct and effective action.

Answer

Increase the batch size

Answer

Add L2 regularization

Answer

Switch from Adam to SGD optimizer

Question 13

A machine learning engineer is using Amazon SageMaker to deploy a model for real-time inference. The model must respond within 100 milliseconds. The initial deployment uses a single ml.m5.large instance, but latency is too high. Which change should the engineer make to reduce latency?

Accepted Answer

Switch to a compute-optimized instance like ml.c5.2xlarge.. A compute-optimized instance like ml.c5.2xlarge provides more CPU and memory, reducing inference latency. Option B is wrong because batch transform is for offline predictions, not real-time; it does not reduce latency for real-time inference. Option C is wrong because using a smaller instance (ml.t2.medium) reduces resources and would likely increase latency, not reduce it. Option D is wrong because multi-model endpoints share resources among models and can lead to contention, potentially increasing latency.

Answer

Use batch transform instead of real-time endpoint.

Answer

Deploy to a single ml.t2.medium instance to reduce cost.

Answer

Deploy the model on a multi-model endpoint.

Question 14

A company is using Amazon SageMaker to host a model for real-time inference. The model is a large ensemble of 10 XGBoost models, each 2 GB. The endpoint uses a single ml.c5.18xlarge instance. The inference latency is high (average 2 seconds). Which change would most effectively reduce latency?

Accepted Answer

Use SageMaker Multi-Model Endpoints to serve each model independently. Serialization/deserialization of large models is a bottleneck; SageMaker Multi-Model Endpoints can reduce overhead by loading only the requested model. Option B (GPU) may not help if the bottleneck is CPU. Option C (Add more instances) helps throughput but not per-request latency. Option D (Batch Transform) is for offline inference.

Answer

Switch to a GPU instance type

Answer

Add more instances behind a load balancer

Answer

Use SageMaker Batch Transform instead of real-time endpoint

Question 15

A machine learning engineer is using SageMaker's built-in XGBoost algorithm for a multi-class classification problem. The training job completes but the model accuracy is low. Which THREE hyperparameters should the engineer tune to improve performance?

Accepted Answer

eta (learning rate). XGBoost hyperparameters: 'num_round' (number of boosting rounds), 'eta' (learning rate), and 'max_depth' (tree depth) are key for improving accuracy. 'subsample' can help but is less direct. 'min_child_weight' also important but these three are most common. 'colsample_bytree' is for feature subsampling.

Answer

subsample

Answer

colsample_bytree