MLA-C01 Practice Test 19 — 15 Questions

Question 1

Which SageMaker built-in algorithm is best suited for detecting anomalous login attempts based on IP addresses and user behavior?

Accepted Answer

IP Insights. IP Insights is a built-in algorithm for learning IP address usage patterns and detecting anomalous behavior. The other algorithms are for different purposes: XGBoost for classification, K-Means for clustering, and PCA for dimensionality reduction.

Answer

XGBoost

Answer

PCA

Answer

K-Means

Question 2

An ML engineer is fine-tuning a foundation model using RLHF on SageMaker. Which THREE components are essential for this workflow? (Select THREE.)

Accepted Answer

A reward model trained on the preference data. RLHF requires a preference dataset for human feedback, a reward model trained on that data, and the PPO algorithm to update the foundation model. The PEFT technique (like LoRA) is often used to make fine-tuning efficient, but it is not strictly essential for RLHF; however, it is commonly used. The base foundation model is required. A validation dataset is needed but not specific to RLHF.

Answer

A large validation dataset for final evaluation

Answer

A PEFT technique like LoRA

Question 3

Which SageMaker feature automatically generates model cards, feature importance, and bias reports without requiring manual coding?

Accepted Answer

SageMaker Autopilot. SageMaker Clarify provides bias detection and feature importance, and it can generate reports. SageMaker Autopilot generates model cards and explanations. SageMaker Experiments tracks experiments. SageMaker Model Monitor is for monitoring. Autopilot is the correct answer because it automates the entire pipeline including model cards and explanations.

Answer

SageMaker Experiments

Answer

SageMaker Clarify

Answer

SageMaker Model Monitor

Question 4

A data scientist is using Amazon SageMaker Data Wrangler to create a data preparation flow. After completing the flow, the scientist wants to export the processed data to a feature group in Amazon SageMaker Feature Store for reuse in multiple training jobs. Which export option should the scientist choose in Data Wrangler?

Accepted Answer

Export to Feature Store. Data Wrangler can directly export processed data to a SageMaker Feature Store feature group, making it available for future training and inference. Exporting to S3 requires manual steps to ingest into Feature Store; saving as a Jupyter notebook or Python script does not directly create a feature group.

Answer

Export to S3

Answer

Export as a Python script

Answer

Save as a Jupyter notebook

Question 5

During a SageMaker training job, the loss stops decreasing and the validation accuracy plateaus early. SageMaker Debugger rules are enabled. Which rule is MOST likely to identify this issue?

Accepted Answer

Overfit rule. The overfit rule detects when validation accuracy plateaus or decreases while training accuracy continues to improve, which is a sign of overfitting. Exploding gradients detects gradient spikes, dead relu detects dead neurons, and weight distribution checks weight distributions but not directly overfitting.

Answer

Weight distribution rule

Answer

Exploding gradients rule

Answer

Dead relu rule

Question 6

A company wants to use SageMaker Autopilot to automatically build a binary classification model. Which output does Autopilot provide to help understand model decisions?

Accepted Answer

An explainability report with feature importance. SageMaker Autopilot generates an explainability report with feature importance. It does not provide a confusion matrix by default; users must evaluate separately. It does not provide SHAP values directly but uses similar techniques. Leaderboard is for ranking trials, not explainability.

Answer

A leaderboard of models with only accuracy metrics

Answer

A confusion matrix for each candidate model

Answer

A SHAP values summary plot for each trial

Question 7

A company wants to use SageMaker to fine-tune a foundation model for a text generation task using RLHF (Reinforcement Learning from Human Feedback). Which THREE components are required in the RLHF pipeline?

Accepted Answer

A pre-trained base model. RLHF typically requires: a pre-trained base model to start, a reward model trained on human preferences, and a reinforcement learning algorithm (like PPO) to update the base model. A LoRA adapter is optional but not required. A classifier is not the same as a reward model.

Answer

A LoRA adapter for parameter-efficient fine-tuning

Answer

A classifier to distinguish generated text from real text

Question 8

A company uses SageMaker Autopilot to build a regression model predicting house prices. After the experiment completes, the company wants to understand why the model makes certain predictions. Which TWO SageMaker features can provide this explainability? (Choose TWO.)

Accepted Answer

SageMaker Clarify. SageMaker Autopilot automatically generates explainability reports. SageMaker Clarify can be used separately for additional analysis. Model Monitor is for drift detection, not explainability. Debugger is for debugging training. Experiments is for tracking.

Answer

SageMaker Model Monitor

Answer

SageMaker Debugger

Answer

SageMaker Experiments

Question 9

A team notices that inference requests to their SageMaker endpoint are failing with '504 Gateway Timeout' for large payloads. What change should be made?

Accepted Answer

Increase the endpoint's invocation timeout. A 504 Gateway Timeout indicates that the SageMaker endpoint's invocation timeout (default 60 seconds) was exceeded while processing a large payload. Increasing the invocation timeout allows the endpoint more time to complete inference for large payloads, resolving the timeout error.

Answer

Enable data capture on the endpoint

Answer

Deploy a shadow endpoint for testing

Answer

Switch to a multi-model endpoint

Question 10

A data science team detects that a deployed model's prediction accuracy is degrading over time due to concept drift. They need to implement a retraining strategy. Which THREE actions are recommended best practices for handling concept drift?

Accepted Answer

Monitor prediction quality using ground truth labels when available.. Monitoring prediction quality using ground truth labels is a fundamental best practice for detecting concept drift. When ground truth labels are available, you can directly measure the model's accuracy over time, which provides the most reliable signal for drift. SageMaker Model Monitor can be configured to capture ground truth data and compare it against predictions to generate quality metrics.

Answer

Automatically roll back to a previous model version upon drift detection.

Answer

Retrain the model on a fixed schedule regardless of performance.

Question 11

A data scientist uses SageMaker Automatic Model Tuning (AMT) with Bayesian optimization to tune an XGBoost model. The objective metric is validation:auc, but the tuning job converges to a plateau early. Which action is MOST effective to improve exploration?

Accepted Answer

Increase the exploration_weight parameter in the tuning configuration. Increasing the exploration/exploitation weight (exploration_weight) in Bayesian optimization encourages the algorithm to try more diverse hyperparameter combinations, avoiding premature convergence.

Answer

Increase the number of max parallel jobs

Answer

Decrease the number of hyperparameters being tuned

Answer

Switch the tuning strategy from Bayesian to Random Search

Question 12

A company uses Amazon SageMaker to train and deploy machine learning models. They need to run batch predictions on 10 TB of data stored in Amazon S3 every night. The model is a PyTorch neural network that fits in GPU memory. The predictions are not time-sensitive, but the job must complete within 8 hours. Which approach would be the MOST cost-effective?

Accepted Answer

Use SageMaker batch transform with a single ml.p3.2xlarge instance.. The most cost-effective because SageMaker batch transform with a single ml.p3.2xlarge instance provides GPU acceleration for the PyTorch neural network, which fits in GPU memory, and can process 10 TB of data within 8 hours. Batch transform automatically handles data partitioning and inference, eliminating the need for custom orchestration, and the single instance avoids the overhead and cost of multiple instances. The ml.p3.2xlarge offers a balance of GPU compute and cost, making it ideal for non-time-sensitive nightly batch jobs.

Answer

Use SageMaker processing job with a script to load the model and run inference.

Answer

Create a real-time endpoint and send all data as a large batch.

Answer

Use multiple ml.c5.4xlarge instances in a batch transform job with custom partitioning.

Question 13

A team is fine-tuning a foundation model using LoRA. They want to reduce memory usage during training. Which technique should they combine LoRA with to further reduce memory?

Accepted Answer

QLoRA. QLoRA combines LoRA with quantization (e.g., 4-bit) to drastically reduce memory. Instruction tuning is a method, not a memory reduction technique. RLHF is a training process. Pruning reduces model size but is not typically combined with LoRA in this context.

Answer

Instruction tuning

Answer

Pruning

Answer

RLHF

Question 14

A company is training a deep learning model for object detection using SageMaker. The training is very slow and the GPU memory is insufficient for the batch size. The team wants to scale across multiple GPUs efficiently. Which THREE actions should they take? (Choose THREE.)

Accepted Answer

Use SageMaker distributed model parallelism. Distributed data parallelism replicates the model and splits batches across GPUs. SageMaker distributed library optimizes this. Model parallelism splits the model when memory is insufficient. Spot instances reduce cost but not speed or memory. Debugger does not speed up training.

Answer

Use managed spot instances

Answer

Enable SageMaker Debugger to identify bottlenecks

Question 15

A company wants to use SageMaker built-in algorithms for a time series forecasting task. Which TWO algorithms are appropriate for this task? (Choose TWO.)

Accepted Answer

DeepAR. DeepAR is specifically designed for time series forecasting. Linear Learner can also be used for forecasting with engineered features. XGBoost can be used for forecasting but is not a built-in algorithm specifically for time series. K-Means is clustering. PCA is dimensionality reduction.

Answer

PCA

Answer

K-Means

Answer

XGBoost