MLA-C01 Practice Test 18 — 15 Questions

Question 1

A data scientist wants to use SageMaker Clarify to analyze bias during training of a binary classification model. Which TWO types of bias metrics can SageMaker Clarify compute? (Select TWO.)

Accepted Answer

Post-training bias metrics (e.g., Difference in Positive Proportions, AD). SageMaker Clarify computes pre-training bias (e.g., class imbalance) and post-training bias (e.g., difference in positive proportions across groups).

Answer

Feature importance

Answer

SHAP values

Answer

Confusion matrix

Question 2

A company wants to build a customer service chatbot that answers questions about their internal policy documents. The documents are updated monthly, and the team cannot afford to retrain a model each time. Which approach is MOST appropriate?

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with the policy documents indexed in a vector store. RAG (Retrieval-Augmented Generation) allows the LLM to retrieve relevant document sections at inference time, so knowledge stays current without retraining. The other options either require expensive retraining for each update or lack document grounding.

Answer

Use a larger foundation model with a longer context window and paste all documents into each prompt

Answer

Fine-tune a base LLM on the policy documents monthly

Answer

Train a custom model from scratch on the policy documents each month

Question 3

Refer to the exhibit. The data scientist wants to update the endpoint to use a new model version without downtime. Which approach should they use?

Accepted Answer

Create a new endpoint configuration with a second variant and update the endpoint. It uses SageMaker's blue/green deployment pattern: creating a new endpoint configuration with a second variant (the new model version) and updating the endpoint shifts traffic gradually or instantly without downtime. This approach leverages the endpoint's ability to host multiple variants and route traffic between them, ensuring zero interruption during the update.

Answer

Delete the existing endpoint and create a new one

Answer

Update the endpoint's model name directly

Answer

Use SageMaker Model Monitor to automatically switch

Question 4

Which SageMaker built-in algorithm is designed for time series forecasting?

Accepted Answer

DeepAR. DeepAR is a built-in algorithm specifically for time series forecasting. BlazingText is for text, XGBoost is for tabular data, and IP Insights is for anomaly detection in IP traffic.

Answer

BlazingText

Answer

IP Insights

Answer

XGBoost

Question 5

A company is fine-tuning a foundation model using RLHF (Reinforcement Learning from Human Feedback) on SageMaker. They want to reduce memory usage and training time. Which THREE techniques should they consider? (Select THREE.)

Accepted Answer

Use a smaller foundation model (e.g., 7B instead of 70B parameters). LoRA/QLoRA reduces trainable parameters, PPO is the standard RLHF algorithm, and using smaller foundation models reduces memory and compute requirements.

Answer

Use SageMaker Data Parallelism with sharded data

Answer

Use full fine-tuning on a larger instance

Question 6

A data scientist is using SageMaker Autopilot to automatically build a binary classification model. The dataset is imbalanced. Which action will Autopilot take by default to address class imbalance?

Accepted Answer

Use class balancing weights. Autopilot automatically applies techniques to handle imbalanced data, such as class balancing weights, when it detects imbalance. It does not require manual configuration. Ensemble selection is part of Autopilot but not specifically for imbalance. SMOTE and undersampling are not built-in defaults.

Answer

Perform random undersampling of the majority class

Answer

Ignore the imbalance and proceed with raw data

Answer

Apply SMOTE oversampling

Question 7

A team is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to find the best configuration as quickly as possible, with a maximum of 50 training jobs. Which TWO strategies should they choose? (Choose TWO.)

Accepted Answer

Use Hyperband with early stopping. Bayesian optimization is efficient for few jobs. Hyperband can be more efficient but early stopping might miss good configurations. Random search is less efficient. Grid search is too exhaustive.

Answer

Use the same objective metric but with different strategies

Answer

Use random search

Answer

Use grid search

Question 8

A team wants to fine-tune a pre-trained Hugging Face transformer model for text classification using SageMaker. They have a custom training script. Which SageMaker estimator should they use?

Accepted Answer

SageMaker Hugging Face estimator. The Hugging Face estimator is the recommended way to run Hugging Face models on SageMaker, as it automatically handles the environment and dependencies.

Answer

SageMaker generic estimator with a custom container

Answer

SageMaker PyTorch estimator

Answer

SageMaker TensorFlow estimator

Question 9

A team is fine-tuning a large language model (LLM) using SageMaker and wants to reduce memory footprint during training. Which technique should they use?

Accepted Answer

Use QLoRA (Quantized Low-Rank Adaptation) with 4-bit quantization. QLoRA (Quantized Low-Rank Adaptation) combines 4-bit quantization with low-rank adapters, significantly reducing GPU memory usage while maintaining model quality.

Answer

Use LoRA (Low-Rank Adaptation) with fp32 precision

Answer

Use SageMaker Model Parallelism with tensor parallelism

Answer

Full fine-tuning on a p3.16xlarge instance

Question 10

A data scientist is evaluating a binary classification model for loan default prediction. Which THREE metrics should they consider to thoroughly assess model performance, especially for imbalanced classes?

Accepted Answer

Recall. For imbalanced classification, accuracy can be misleading. AUC (Area under ROC curve) is robust to imbalance, F1 balances precision and recall, and recall (true positive rate) is important to catch defaults. RMSE and R² are for regression, NDCG is for ranking.

Answer

R²

Answer

RMSE

Question 11

Which SageMaker feature provides AutoML capabilities, including automatic data preprocessing, model selection, and hyperparameter tuning?

Accepted Answer

SageMaker Autopilot. SageMaker Autopilot automates the ML pipeline from data to model, including preprocessing, algorithm selection, and tuning.

Answer

SageMaker Data Wrangler

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Experiments

Question 12

A machine learning engineer is using Amazon SageMaker Debugger to monitor a training job for a deep neural network. They receive a rule alert indicating 'exploding gradients'. Which action should they take to address this issue?

Accepted Answer

Reduce the learning rate. Exploding gradients occur when gradients become too large, causing instability. Reducing the learning rate mitigates this. Increasing batch size can also help by smoothing gradients, but reducing learning rate is a direct solution.

Answer

Use a smaller batch size

Answer

Increase the number of layers to absorb gradients

Answer

Increase the learning rate

Question 13

An ML team is using SageMaker Automatic Model Tuning to optimize hyperparameters for a neural network. They want to prioritize exploration of the hyperparameter space early in the tuning process. Which strategy should they choose?

Accepted Answer

Bayesian optimization. Bayesian optimization balances exploration and exploitation, but early in the process it tends to explore more. Random search explores uniformly without adaptation. Hyperband focuses on early stopping. Grid search is exhaustive. Bayesian optimization is the best choice for systematic exploration.

Answer

Grid search

Answer

Random search

Answer

Hyperband

Question 14

A company is using SageMaker Pipelines to orchestrate their ML workflow. They have a Condition step that checks if a model's accuracy exceeds 0.9. If true, they want to register the model in the model registry; otherwise, they want to run a retraining step. Which step type should they use for the decision?

Accepted Answer

Condition step. The Condition step in SageMaker Pipelines allows you to choose between two branches based on a condition. The other options are not designed for branching: Transform is for batch inference, Tuning is for hyperparameter optimization, and Processing is for data processing.

Answer

Transform step

Answer

Processing step

Answer

Tuning step

Question 15

An ML team wants to use Amazon SageMaker Ground Truth to create a labeled dataset for a multi-class image classification task. They have a large set of unlabeled images and want to minimize labeling costs while maintaining high accuracy. Which Ground Truth feature should they enable?

Accepted Answer

Active learning. Active learning in SageMaker Ground Truth automatically selects the most informative unlabeled images for human labeling, reducing the total number of labels needed while maintaining model accuracy. By iteratively training a model on a small labeled subset and then using that model to identify uncertain predictions, the system focuses labeling effort on the data that will most improve the model, directly minimizing labeling costs.

Answer

Annotation consolidation

Answer

Data labeling workforce management

Answer

Consolidated labeling