MLA-C01 ML Model Development Practice Test 2 — 15 Questions

Question 1

A practitioner is using SageMaker Automatic Model Tuning with Hyperband strategy. They want to stop underperforming trials early to save compute. Which Hyperband parameter controls the aggressiveness of early stopping?

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

Accepted Answer

early_stopping_type

Answer

strategy

Answer

max_jobs

Answer

max_parallel_jobs

Question 2

A company needs to perform time-series forecasting on historical sales data. Which SageMaker built-in algorithm is BEST suited for this task?

Accepted Answer

DeepAR

Answer

BlazingText

Answer

Linear Learner

Answer

XGBoost

Question 3

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?

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 4

A machine learning engineer wants to reduce costs for hyperparameter tuning jobs that run for several hours. The jobs are fault-tolerant and can be interrupted. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Enable SageMaker Managed Spot Training

Answer

Use on-demand instances for reliability

Answer

Use ml.c5 instances instead of ml.p3

Answer

Increase the number of parallel training jobs

Question 5

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 6

A data scientist wants to train a binary classification model using Amazon SageMaker with a built-in algorithm that performs well on tabular data. Which algorithm should they choose?

Accepted Answer

XGBoost. XGBoost is a popular built-in algorithm in SageMaker for classification and regression on tabular data. Linear Learner is also for tabular data but XGBoost often performs better for complex patterns.

Answer

Image Classification

Answer

DeepAR

Answer

BlazingText

Question 7

A machine learning engineer needs to reduce costs when training a large model on SageMaker. They are willing to accept potential interruptions and have checkpointing enabled. Which instance purchasing option should they use?

Accepted Answer

Spot instances. Spot instances offer significant cost savings (up to 60-90%) compared to on-demand, but can be reclaimed by AWS with a 2-minute notice. Checkpointing allows resuming training from the last saved state, making spot instances suitable.

Answer

Reserved instances

Answer

Dedicated hosts

Answer

On-demand instances

Question 8

A data scientist is using SageMaker Automatic Model Tuning to find the best hyperparameters for a model. They want to reduce the total tuning time for a given number of training jobs. Which tuning strategy should they choose?

Accepted Answer

Hyperband. Hyperband is an early stopping strategy that allocates resources to promising configurations and stops poor performers early, reducing total tuning time compared to random search or Bayesian optimization without early stopping.

Answer

Grid search

Answer

Random search

Answer

Bayesian optimization

Question 9

A team is training a large language model on SageMaker using PyTorch with data parallelism. The model is too large to fit on a single GPU. Which distributed training strategy should they use to split the model across multiple GPUs?

Accepted Answer

Model parallelism. Model parallelism splits the model itself across devices, which is necessary when the model is too large for one GPU. SageMaker's model parallelism library supports this.

Answer

Tensor parallelism

Answer

Data parallelism

Answer

Pipeline parallelism

Question 10

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare different hyperparameter configurations and visualize the impact on model accuracy. What should they use to track hyperparameters?

Accepted Answer

SageMaker Experiments. SageMaker Experiments allows you to log hyperparameters as parameters. They can be viewed and compared across runs in the SageMaker Studio UI.

Answer

SageMaker Debugger

Answer

SageMaker Autopilot

Answer

SageMaker Model Monitor

Question 11

A company is using SageMaker Autopilot to automatically build a regression model on a dataset. They want to understand which features are most important for the model's predictions. Which feature of Autopilot can provide this insight?

Accepted Answer

Autopilot explainability report. SageMaker Autopilot can generate explainability reports that include feature importance, either through SHAP or other methods, depending on the model type.

Answer

Autopilot candidate definition notebook

Answer

Autopilot model leaderboard

Answer

Autopilot data exploration report

Question 12

A machine learning engineer is training a model using SageMaker and wants to set up monitoring to detect if gradients become too large, which could destabilize training. Which SageMaker Debugger built-in rule should they enable?

Accepted Answer

ExplodingGradients. Debugger's built-in rule 'ExplodingGradients' monitors gradient norms and alerts if they exceed a threshold, helping to stabilize training.

Answer

DeadRelu

Answer

LossNotDecreasing

Answer

Overfit

Question 13

A data scientist wants to fine-tune a large language model for a question-answering task. They want to reduce memory usage during training by using a low-rank approximation of the weight updates. Which technique should they use?

Accepted Answer

LoRA. LoRA (Low-Rank Adaptation) adds low-rank matrices to model weights, significantly reducing memory footprint while achieving competitive performance. QLoRA adds quantization for further reduction.

Answer

Full fine-tuning

Answer

Instruction tuning

Answer

RLHF

Question 14

A team is building a fraud detection model using SageMaker and wants to detect anomalies in user login events. Which SageMaker built-in algorithm is specifically designed for anomaly detection in event-based data?

Accepted Answer

IP Insights. IP Insights is a built-in algorithm for learning representations of IP addresses and detecting anomalous login patterns, commonly used for fraud detection.

Answer

Factorisation Machines

Answer

Random Cut Forest

Answer

K-Means

Question 15

A data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing model performance?

Accepted Answer

AUC. AUC (Area Under the ROC Curve) is robust to class imbalance as it evaluates the model's ability to rank positive vs negative examples. Precision, recall, and F1 can be misleading if not threshold-optimized.

Answer

Precision

Answer

Accuracy

Answer

Recall