MLA-C01 Practice Test 17 — 15 Questions

Question 1

A data scientist needs to run a hyperparameter tuning job for a PyTorch model using SageMaker. They want to use Hyperband for efficient resource allocation. Which tuning strategy should they select in the HyperparameterTuner?

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

Accepted Answer

Hyperband. SageMaker Automatic Model Tuner supports Bayesian, Random, and Hyperband strategies. Hyperband is an early stopping-based method that allocates resources adaptively. The 'Hyperband' strategy should be selected explicitly.

Answer

Bayesian optimization

Answer

Random search

Answer

Grid search

Question 2

A company is using SageMaker Debugger to monitor a training job for a deep learning model. They want to detect when gradients become extremely large, which may cause training instability. Which built-in rule should they use?

Accepted Answer

ExplodingGradients. The ExplodingGradients rule monitors gradient norms and raises an alert if they exceed a threshold.

Answer

DeadRelu

Answer

VanishingGradients

Answer

Overfit

Question 3

A team is fine-tuning a foundation model using reinforcement learning from human feedback (RLHF) on SageMaker. They have a dataset of human preferences. Which SageMaker capability is most suitable for the reward model training step?

Accepted Answer

SageMaker Training with a custom PyTorch container. RLHF typically involves training a reward model on human preference data. SageMaker can be used to train any custom model, including a reward model, using its training jobs with a PyTorch or TensorFlow estimator.

Answer

SageMaker JumpStart

Answer

SageMaker Ground Truth

Answer

SageMaker Autopilot

Question 4

A data scientist suspects that a deep learning model is overfitting. They enable SageMaker Debugger and want to detect overfitting automatically. Which built-in rule should they use?

Accepted Answer

Overfit. The overfit rule in SageMaker Debugger monitors training and validation loss divergence, a key indicator of overfitting.

Answer

ExplodingGradients

Answer

PoorWeightInitialization

Answer

DeadRelu

Question 5

A data scientist is using SageMaker Automatic Model Tuning to optimize hyperparameters for an XGBoost model. They want to maximize AUC. Which search strategy is MOST appropriate for efficient exploration?

Accepted Answer

Bayesian optimization

Answer

Random search

Answer

Grid search

Answer

Hyperband

Question 6

A company is training a large computer vision model using SageMaker. The training dataset is 500 GB and the model has 1 billion parameters. The team needs to minimize training time. Which distributed training strategy should they use?

Accepted Answer

Model parallelism. Model parallelism splits the model layers across multiple GPUs, which is necessary when the model is too large to fit on a single GPU. Data parallelism replicates the model on each GPU and splits the data, but is limited by the memory of a single GPU.

Answer

Pipeline parallelism

Answer

Sharded data parallelism

Answer

Data parallelism

Question 7

A company is using SageMaker to train a model for image classification. The training dataset contains 100,000 labeled images. The team wants to use a pre-trained model to reduce training time. Which SageMaker feature should they use?

Accepted Answer

SageMaker JumpStart. SageMaker JumpStart provides pre-trained models that can be fine-tuned on custom datasets, reducing training time and data requirements.

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker built-in Image Classification algorithm

Question 8

A data scientist is using SageMaker Experiments to track multiple training runs. They want to compare runs based on the objective metric and visualize performance. Which THREE steps should they perform? (Choose THREE.)

Accepted Answer

Use SageMaker Studio Experiments UI to list and compare trials. To track and compare runs, you create an experiment, log parameters and metrics, and then use the Experiments UI or SDK to list and compare trials.

Answer

Deploy the best model to an endpoint

Answer

Enable SageMaker Model Monitor for each run

Question 9

A company is fine-tuning a large language model using LoRA with a Hugging Face estimator in SageMaker. They want to reduce memory usage during training. Which instance type is most cost-effective for this workload?

Accepted Answer

ml.g5.xlarge. LoRA reduces the number of trainable parameters, allowing training on smaller GPUs. ml.g5 instances are optimized for machine learning inference and training with a good price-performance for fine-tuning.

Answer

ml.p4d.24xlarge

Answer

ml.c5.2xlarge

Answer

ml.trn1.2xlarge

Question 10

A machine learning engineer is using SageMaker Autopilot for AutoML. Which TWO outputs does Autopilot produce?

Accepted Answer

An ensemble of candidate models

Answer

A hyperparameter tuning job summary

Answer

A data labeling pipeline

Answer

A single optimal model

Question 11

A team is fine-tuning a Hugging Face BERT model for text classification using SageMaker. They want to use the Hugging Face estimator for convenience. Which parameter must be set to use a custom training script?

Accepted Answer

entry_point. The entry_point parameter specifies the path to the training script. The instance_type is for hardware selection. The hyperparameters dictionary passes parameters to the script. The framework_version specifies the Hugging Face version.

Answer

framework_version

Answer

instance_type

Answer

hyperparameters

Question 12

A machine learning engineer is training a TensorFlow model using SageMaker with distributed training. They need to implement data parallelism across multiple GPUs. Which SageMaker feature should they use to distribute the training?

Accepted Answer

SageMaker Distributed Data Parallelism. SageMaker's distributed data parallelism library handles splitting data across GPUs and synchronizing gradients, optimized for TensorFlow and PyTorch.

Answer

SageMaker Automatic Model Tuning

Answer

SageMaker Debugger

Answer

SageMaker Model Parallelism

Question 13

A data scientist wants to bring a custom PyTorch model to SageMaker. Which THREE methods are valid?

Accepted Answer

Use Script mode with the PyTorch Estimator

Answer

Use SageMaker Autopilot

Answer

Use the built-in Image Classification algorithm

Question 14

A data scientist is training an XGBoost model on a large dataset using a SageMaker Training Job. They want to minimize costs without sacrificing model performance. Which instance type and training strategy should they choose?

Accepted Answer

Use a single ml.g4dn.xlarge Spot instance with no distributed training. Using Spot instances with Managed Spot Training can reduce costs by up to 90% compared to On-Demand, and SageMaker automatically handles interruptions. For single-instance training, a single ml.g4dn.xlarge provides sufficient compute for moderate-sized datasets.

Answer

Use a single ml.m5.large On-Demand instance with model parallelism

Answer

Use multiple ml.trn1.2xlarge On-Demand instances with data parallelism

Answer

Use a single ml.p3.2xlarge On-Demand instance with data parallelism

Question 15

A data scientist wants to use SageMaker Autopilot to automatically build a regression model. The dataset contains 200 features and 50,000 rows. Which output does SageMaker Autopilot provide?

Accepted Answer

A leaderboard of candidate models with metrics and explainability reports. SageMaker Autopilot automatically explores various algorithms and preprocessing steps, then provides a leaderboard of candidate models with metrics.

Answer

Only the best model without any metrics

Answer

A single optimal model with no further tuning

Answer

A Python script for manual training