MLA-C01 Practice Test 14 — 15 Questions

Question 1

A team is fine-tuning a foundation model using LoRA in SageMaker. They want to reduce memory usage during training. Which instance type is optimized for cost-effective fine-tuning with LoRA?

Accepted Answer

ml.g5.2xlarge

Answer

ml.p3.2xlarge

Answer

ml.c5.2xlarge

Answer

ml.trn1.2xlarge

Question 2

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 3

A company wants to deploy a new model using a canary deployment strategy on SageMaker. Which two actions should they take? (Select TWO.)

Accepted Answer

Create a new endpoint with two production variants. To implement canary deployment, create two production variants (current and new) with initial traffic weights (e.g., 95% and 5%), then update the endpoint to gradually shift traffic. Using endpoint update with routing config adjusts traffic weights over time.

Answer

Register both models in the Model Registry with 'Approved' status

Answer

Use SageMaker Model Monitor to compare model performance

Answer

Enable data capture on the endpoint

Question 4

A machine learning team needs to deploy a PyTorch model that has been compiled with SageMaker Neo to improve inference performance on edge devices. Which TWO statements about SageMaker Neo are correct? (Select TWO.)

Accepted Answer

Neo reduces model inference latency through optimization techniques. SageMaker Neo optimizes models for specific hardware targets (e.g., ARM, Intel, NVIDIA) and reduces latency. It does not require training frameworks; it compiles trained models. It does not automatically scale endpoints. It is not limited to built-in algorithms.

Answer

Neo requires the model to be trained on SageMaker

Answer

Neo can only compile models trained with SageMaker built-in algorithms

Answer

Neo automatically scales SageMaker endpoints based on demand

Question 5

An MLOps team is designing a SageMaker Pipeline to automate model retraining. The pipeline must: (1) run training only if new training data is available, (2) register the model in SageMaker Model Registry only if evaluation metrics exceed a threshold, (3) deploy the approved model to a staging endpoint automatically. Which THREE steps should they include? (Choose THREE.)

Accepted Answer

ConditionStep to check if evaluation metrics exceed the threshold. A ConditionStep evaluates a condition and routes to different branches. The RegisterModel step registers a model in the registry. A TransformStep (batch transform) runs inference, but for deployment a CreateModel and endpoint creation step is needed, though not listed; the correct deploy step is not a TransformStep. A ProcessingStep can be used to check for new data. A TrainingStep trains the model. A TuningStep is for hyperparameter tuning.

Answer

TuningStep to perform hyperparameter optimization

Answer

TransformStep to deploy the model to a staging endpoint

Question 6

A team is training a PyTorch model using SageMaker. They have a custom training script that requires specific Python packages not included in the SageMaker default PyTorch container. Which approach should they use?

Accepted Answer

Use the built-in PyTorch estimator and specify a requirements.txt in the source directory. Using a SageMaker PyTorch estimator with a requirements.txt file allows installing additional packages on top of the official container. This is simpler than building a custom container.

Answer

Build a custom Docker container from scratch and push it to Amazon ECR

Answer

Use the SageMaker XGBoost estimator and modify the script to use PyTorch

Answer

Use SageMaker Autopilot to automatically handle dependencies

Question 7

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 8

A machine learning engineer runs a training job and notices the loss is NaN after a few steps. Which SageMaker Debugger rule can help identify this issue?

Accepted Answer

Exploding gradients

Answer

Overfit

Answer

Dead ReLU

Answer

Class imbalance

Question 9

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 10

A company is using SageMaker to train a large model using data parallelism with the SageMaker distributed data parallelism library. They notice that the training throughput is not scaling linearly with the number of GPUs. Which THREE factors could be causing this?

Accepted Answer

I/O bottleneck from reading data from Amazon S3. Communication overhead from gradient synchronization, I/O bottlenecks from reading data, and an inefficient loss scaling strategy can all limit scaling. Model size alone is not a scaling issue if it fits on GPUs. Instance type differences affect speed but not scaling linearity directly.

Answer

Using different instance types across the cluster

Answer

Model size too large for the GPUs

Question 11

A machine learning engineer wants to reduce training costs by using excess EC2 capacity. Which instance purchasing option should they choose for SageMaker training jobs?

Accepted Answer

Spot Instances

Answer

Reserved Instances

Answer

On-Demand Instances

Answer

Dedicated Instances

Question 12

A machine learning engineer is using SageMaker Debugger to monitor training jobs. They want to capture tensors every 100 steps but only for the first 500 steps. Which configuration should they set in the Debugger hook?

Accepted Answer

collection_configs with save_interval=100 and end_step=500

Answer

collection_configs with save_interval=500 and end_step=100

Answer

collection_configs with start_step=100 and end_step=500

Answer

Use SageMaker Debugger rules to filter steps

Question 13

A team is training a large language model using SageMaker with multiple GPUs. They need to reduce training time by splitting the model across devices due to memory constraints. Which distributed training strategy should they use?

Accepted Answer

Model parallelism

Answer

SageMaker Distributed Data Parallel (SMDDP)

Answer

Data parallelism

Answer

SageMaker Distributed Model Parallel (SMDMP)

Question 14

A team wants to use a custom PyTorch training script in SageMaker. They need to install additional Python packages not included in the base PyTorch container. Which approach should they take?

Accepted Answer

Use the SageMaker PyTorch estimator with a requirements.txt file

Answer

Use SageMaker Script Mode with a custom Dockerfile

Answer

Build a custom container with Docker

Answer

Install packages using a lifecycle configuration

Question 15

A company needs to detect bias in a pre-trained model before deployment. They want to compute metrics like disparate impact and equal opportunity difference. Which AWS service should they use?

Accepted Answer

SageMaker Clarify

Answer

Amazon Rekognition

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger