MLA-C01 Practice Test 16 — 15 Questions

Question 1

A data scientist is training an object detection model using SageMaker built-in Object Detection algorithm. They want to visualize the bounding boxes on validation images after training. Which approach should they use?

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

Accepted Answer

Write a custom inference script that saves images with bounding boxes

Answer

Use SageMaker Debugger to capture output tensors

Answer

Enable SageMaker Model Monitor

Answer

Use SageMaker Clarify

Question 2

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

Accepted Answer

DeepAR

Answer

Linear Learner

Answer

Factorisation Machines

Answer

BlazingText

Question 3

A data scientist is evaluating a binary classification model. They have the confusion matrix and want to assess the model's performance comprehensively. Which THREE metrics should they consider? (Select THREE.)

Accepted Answer

Precision

Answer

RMSE

Answer

R²

Question 4

A company uses SageMaker Autopilot to build a binary classification model. The generated leaderboard shows an ensemble model as the best candidate. The team needs a model that can be deployed for real-time inference with latency < 10ms. What should they do?

Accepted Answer

Select the best single model from the leaderboard (non-ensemble candidate) and deploy it. Autopilot ensemble models may have high latency due to combining multiple models. The team should evaluate non-ensemble candidates (single model) from the leaderboard to meet latency requirements.

Answer

Use SageMaker Inference Recommender to profile the ensemble model and optimize it

Answer

Deploy the ensemble model as a SageMaker endpoint; ensemble models are optimized for low latency

Answer

Retrain the ensemble model with fewer base estimators using a custom container

Question 5

A data scientist is using SageMaker to train a custom PyTorch model for image classification. They want to use SageMaker Debugger to detect training issues. Which TWO built-in rules are most relevant for detecting common training problems? (Select TWO.)

Accepted Answer

Overfit. ExplodingGradients detects gradients becoming too large, and Overfit detects when validation loss diverges from training loss. Both are common issues.

Answer

DataDistribution

Answer

ImageQuality

Answer

ConfusionMatrix

Question 6

A data scientist wants to track hyperparameters, metrics, and artifacts for multiple training runs in SageMaker. They need to compare runs and identify the best performing model. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments allows tracking and comparing runs, including hyperparameters, metrics, and artifacts.

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger

Answer

SageMaker Autopilot

Question 7

Which SageMaker feature allows you to automatically tune hyperparameters using Bayesian optimization?

Accepted Answer

SageMaker Automatic Model Tuning. SageMaker Automatic Model Tuning (AMT) supports Bayesian optimization, random search, and Hyperband. Debugger is for monitoring. Experiments is for tracking. Autopilot is for AutoML.

Answer

SageMaker Autopilot

Answer

SageMaker Experiments

Answer

SageMaker Debugger

Question 8

A machine learning engineer is using SageMaker Automatic Model Tuning to optimize hyperparameters for a regression model. The objective metric is RMSE. The training job is costly, and the engineer wants to find a good configuration quickly. Which tuning strategy should they use?

Accepted Answer

Bayesian optimization. Bayesian optimization builds a probabilistic model of the objective function and selects hyperparameters to try next based on past results, making it more efficient than random search. Hyperband is a bandit-based approach that may be faster but can be less stable.

Answer

Hyperband

Answer

Random search

Answer

Grid search

Question 9

A team is training a PyTorch model using SageMaker and wants to use their own custom training container with a specific PyTorch version. Which approach should they use?

Accepted Answer

Use SageMaker Bring Your Own Container (BYOC) with a custom Docker image. BYOC (Bring Your Own Container) allows teams to package their own environment, including custom PyTorch versions, into a Docker container and use it with SageMaker.

Answer

Use the SageMaker built-in PyTorch estimator and set the framework_version

Answer

Use SageMaker Script Mode with a PyTorch script

Answer

Use SageMaker Autopilot to automatically select the container

Question 10

A company wants to detect anomalies in login events from a large user base, focusing on unusual patterns that may indicate compromised accounts. Which SageMaker built-in algorithm is most suitable for this task?

Accepted Answer

IP Insights. IP Insights is designed for anomaly detection in IP address usage, learning typical login patterns and flagging unusual ones. The other algorithms are not specialized for this use case.

Answer

K-Means

Answer

DeepAR

Answer

Factorisation Machines

Question 11

A machine learning engineer is preparing a training job on SageMaker with a custom Docker container. Which TWO actions are required to use the container with SageMaker? (Choose TWO.)

Accepted Answer

Push the container image to Amazon ECR. To use a custom container, you must push it to Amazon ECR and specify the registry path in the estimator. The container must also implement the SageMaker training contract (like /opt/ml), but that is part of building the image.

Answer

Upload the container image to Amazon S3

Answer

Enable SageMaker Debugger to monitor the custom container

Answer

Register the container in SageMaker Model Registry

Question 12

A team wants to evaluate a binary classification model for credit risk. They need to understand the trade-off between false positives and false negatives. Which TWO metrics should they use? (Select TWO.)

Accepted Answer

Recall. Precision and recall are complementary; precision measures false positives, recall measures false negatives. AUC-ROC summarizes the trade-off across thresholds. RMSE is for regression. NDCG is for ranking.

Answer

NDCG

Answer

AUC-ROC

Answer

RMSE

Question 13

A machine learning engineer wants to automatically track hyperparameters, metrics, and artifacts for multiple training runs. Which SageMaker feature should they use?

Accepted Answer

SageMaker Experiments. SageMaker Experiments is purpose-built for tracking and comparing training runs, capturing parameters, metrics, and artifacts.

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker Clarify

Question 14

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

Accepted Answer

DeepAR. DeepAR is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks. The other algorithms are for different tasks: XGBoost for classification/regression, BlazingText for NLP, and Image Classification for computer vision.

Answer

Image Classification

Answer

BlazingText

Answer

XGBoost

Question 15

A data scientist trains a binary classification model using SageMaker and obtains an AUC of 0.95 on the test set. However, the precision-recall curve shows low precision for high recall thresholds. The business requires a model that performs well on the minority class. Which metric should the team primarily optimize during hyperparameter tuning?

Accepted Answer

F1-score on the validation set. For imbalanced datasets, the F1-score balances precision and recall, making it a better objective than AUC, which can be misleading when class imbalance exists.

Answer

Accuracy

Answer

AUC (Area Under the ROC Curve)

Answer

Log loss