MLA-C01 Practice Test 15 — 15 Questions

Question 1

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?

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

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 2

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?

Accepted Answer

early_stopping_type

Answer

strategy

Answer

max_jobs

Answer

max_parallel_jobs

Question 3

A team is fine-tuning a Hugging Face transformer model on SageMaker. They need to use a custom training script with the Hugging Face Estimator. Which SageMaker feature does this represent?

Accepted Answer

Script mode

Answer

Built-in algorithm

Answer

SageMaker Autopilot

Answer

SageMaker Debugger

Question 4

A team deploys a machine learning model using a SageMaker endpoint with an ML.T4 instance. After a week, they notice that the endpoint's CPU utilization is consistently below 10% and latency is low. However, the endpoint is incurring high costs. Which action should the team take to reduce costs while maintaining the ability to serve traffic?

Accepted Answer

Migrate to a SageMaker Serverless Inference endpoint. The endpoint's CPU utilization is consistently below 10% with low latency, indicating that traffic is sparse and the instance is severely underutilized. SageMaker Serverless Inference automatically scales compute resources based on request volume and charges only for the compute time consumed per inference, eliminating idle costs. This makes it the most cost-effective choice for low-utilization workloads while still maintaining the ability to serve traffic on demand.

Answer

Switch to a multi-model endpoint to share instances across models

Answer

Reduce the number of instances to one

Answer

Implement an asynchronous inference endpoint

Question 5

A company is fine-tuning a large language model using reinforcement learning from human feedback (RLHF). Which THREE components are typically required?

Accepted Answer

A reference model

Answer

A discriminative classifier

Answer

A value function

Question 6

A company uses SageMaker Neo to compile a trained model for deployment on edge devices. What is the primary benefit of using Neo?

Accepted Answer

It reduces model size and improves inference speed on target hardware. SageMaker Neo optimizes models for specific hardware architectures (e.g., ARM, Intel, NVIDIA) to achieve faster inference and lower memory footprint.

Answer

It monitors model drift in production

Answer

It automatically retrains the model on new data

Answer

It provides a serverless inference endpoint

Question 7

A data scientist is using SageMaker built-in XGBoost algorithm for a binary classification task. Which objective metric is MOST appropriate for SageMaker Automatic Model Tuning to maximize?

Accepted Answer

validation:auc

Answer

validation:mae

Answer

validation:rmse

Answer

validation:ndcg

Question 8

A team is fine-tuning a foundation model using LoRA for a text summarization task. They want to reduce memory footprint during training. Which technique should they combine with LoRA?

Accepted Answer

QLoRA

Answer

Data parallelism

Answer

Gradient checkpointing

Answer

Mixed precision

Question 9

A data scientist wants to quickly build a binary classification model without writing any code. Which SageMaker feature is MOST suitable?

Accepted Answer

SageMaker Autopilot

Answer

SageMaker Debugger

Answer

SageMaker Model Monitor

Answer

SageMaker Ground Truth

Question 10

A machine learning engineer is using SageMaker Debugger to detect if a neural network has dead ReLU units during training. Which built-in rule should they enable?

Accepted Answer

DeadRelu. The 'DeadRelu' rule in Debugger monitors the fraction of ReLU activations that are zero and alerts if too many neurons are dead.

Answer

Overfit

Answer

ExplodingGradients

Answer

LossNotDecreasing

Question 11

A company has 200 small PyTorch models that are each used infrequently but need to be available for real-time inference. To minimize costs, they want to host all models on a single endpoint. Which SageMaker feature should they use?

Accepted Answer

Multi-model endpoint (MME). Multi-model endpoints allow hosting hundreds of models on a single endpoint, automatically loading/unloading models based on traffic. Multi-container endpoints are for different containers, not multiple models. Batch and asynchronous are not real-time.

Answer

Multi-container endpoint

Answer

Batch Transform job

Answer

Asynchronous inference endpoint

Question 12

A data science team is deploying a PyTorch model for real-time inference with sub-second latency requirements. They need to minimize cost while handling variable traffic. Which TWO approaches should they consider? (Choose TWO.)

Accepted Answer

Compile the model with SageMaker Neo. Serverless inference auto-scales to zero when not in use and charges per request, minimizing cost for variable traffic. SageMaker Neo compiles the model for optimal hardware performance, achieving low latency. Multi-model endpoints (MME) are for hosting multiple models, not single-model optimization. Elastic Inference adds GPU acceleration at lower cost than a full GPU instance, but with Neo compilation the team may not need it. Batch transform is for offline, not real-time.

Answer

Attach Amazon Elastic Inference to a real-time endpoint

Answer

Use a batch transform job to process requests in batches

Answer

Use a multi-model endpoint (MME) to host the model

Question 13

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 14

A team uses MLflow on SageMaker for experiment tracking. They want to automate the retraining of a model when new training data arrives in an S3 bucket. Which combination of services should they use?

Accepted Answer

EventBridge -> Lambda -> SageMaker Training Job. EventBridge can detect S3 PutObject events and trigger a Lambda function that starts a SageMaker training job, possibly using MLflow for tracking.

Answer

SageMaker Pipelines scheduled trigger every hour

Answer

S3 Event Notifications -> SQS -> SageMaker Training Job

Answer

AWS Step Functions with S3 poller

Question 15

A company wants to use SageMaker Autopilot for a regression problem. They require an explainability report that shows feature importance globally. Which Autopilot feature should they enable?

Accepted Answer

Explainability report generation

Answer

AutoML candidate generation

Answer

Ensembling mode

Answer

Hyperparameter optimization