MLA-C01 Practice Test 24 — 15 Questions

Question 1

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.)

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

Accepted Answer

Use SageMaker distributed model parallelism. Option A is correct because SageMaker distributed model parallelism shards the model itself across multiple GPUs, which directly addresses the insufficient GPU memory problem by allowing a model too large for a single GPU to be split across devices, and it also speeds up training of large models. Option B is correct because SageMaker distributed data parallelism splits each mini-batch across GPUs and uses AllReduce for efficient gradient synchronization, enabling the team to scale the effective batch size and throughput across multiple GPUs efficiently. Option D is correct because the SageMaker distributed training configuration via the SageMaker SDK (e.g., the Distribution parameter with smdistributed settings in the estimator) is the required mechanism to actually enable and launch model or data parallelism on the training cluster. Option C is not correct because managed spot instances reduce cost, not training time or GPU memory constraints, and can even introduce interruptions. Option E is not correct because SageMaker Debugger only monitors and reports training bottlenecks; it does not itself scale training across multiple GPUs or resolve memory limitations.

Answer

Use managed spot instances

Answer

Enable SageMaker Debugger to identify bottlenecks

Question 2

A company uses Amazon SageMaker Ground Truth to label a dataset for object detection. To reduce labeling costs, they want to use active learning. Which configuration should they set up in Ground Truth?

Accepted Answer

Enable automated data labeling with a pre-trained model to select uncertain samples. Active learning in Amazon SageMaker Ground Truth reduces labeling costs by automatically selecting the most uncertain or informative data samples for human review, rather than labeling all data. Option D correctly configures automated data labeling with a pre-trained model to select uncertain samples, which is the core mechanism of active learning in Ground Truth.

Answer

Use a private workforce to label all data manually

Answer

Set the labeling job to random sampling of data

Answer

Configure the labeling job to use only bounding box annotations

Question 3

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. The ExplodingGradients built-in rule in SageMaker Debugger is specifically designed to detect when gradient values become excessively large during training, which can cause numerical instability and prevent convergence. It monitors the gradients tensor and triggers if the ratio of the maximum absolute gradient to the average absolute gradient exceeds a threshold (default 10.0). This directly addresses the engineer's requirement to detect destabilizing gradients.

Answer

DeadRelu

Answer

LossNotDecreasing

Answer

Overfit

Question 4

A data science team deploys a regression model using Amazon SageMaker. After one week, the model's prediction accuracy drops significantly. The team needs to detect this degradation automatically and trigger retraining. Which AWS service should they use to monitor the model's performance over time and set up alerts?

Accepted Answer

Amazon SageMaker Model Monitor. Amazon SageMaker Model Monitor is the correct choice because it is purpose-built to continuously monitor machine learning models deployed on SageMaker endpoints for data drift, feature attribution drift, and prediction quality degradation. It automatically compares live inference data against a baseline, triggers alerts when performance drops, and can be configured to initiate retraining pipelines via AWS Lambda or Step Functions, directly addressing the need to detect accuracy degradation and trigger retraining.

Answer

AWS CloudWatch

Answer

Amazon Inspector

Answer

AWS Config

Question 5

A team uses SageMaker Neo to compile a model for deployment on a target device. After compilation, they deploy the compiled model to a SageMaker endpoint using the Neo-optimized container. The endpoint fails to start with error "RuntimeError: Unable to load model". What could be the issue?

Accepted Answer

The target device architecture during compilation does not match the endpoint instance architecture.. SageMaker Neo compiles a model for a specific target architecture (e.g., ARM, x86, GPU). When deploying the compiled model to a SageMaker endpoint, the endpoint instance type must have a CPU or accelerator architecture that matches the target device specified during compilation. If they do not match, the Neo-optimized runtime cannot load the compiled binary, resulting in a 'RuntimeError: Unable to load model'.

Answer

The compiled model was not uploaded to the correct S3 path.

Answer

The Neo compilation job failed silently.

Answer

The endpoint instance type does not support Neo.

Question 6

Which TWO actions are recommended best practices for securing an Amazon SageMaker notebook instance? (Select TWO.)

Accepted Answer

Use AWS KMS to encrypt the notebook instance's storage volume.. Option C is correct because SageMaker notebook instances store data on an attached EBS volume, and AWS best practices recommend encrypting that storage with an AWS KMS customer managed key to protect data at rest, including notebooks, scripts, and artifacts. Option E is correct because disabling direct internet access forces the notebook instance to route traffic through a VPC (with NAT gateway or VPC endpoints), which prevents unauthorized outbound access and helps protect against data exfiltration while still allowing controlled access to AWS services. Option A is not a recommended best practice for securing a notebook instance because network ACLs are stateless subnet-level controls and do not govern SageMaker API calls, which are authorized via IAM policies and VPC endpoint policies. Option B is incorrect because Multi-AZ deployment is not a feature of SageMaker notebook instances; they run in a single Availability Zone. Option D is incorrect because placing a notebook instance in a public subnet with an internet gateway exposes it to inbound internet traffic and violates the recommendation to keep notebook instances in private subnets without direct internet access.

Answer

Use network ACLs to restrict API calls to the SageMaker API.

Answer

Enable Multi-AZ deployment for the notebook instance.

Answer

Associate the notebook instance with a public subnet that has an internet gateway.

Question 7

During data preparation for a regression model, a data scientist notices that two features have a Pearson correlation coefficient of 0.95. The scientist is concerned about multicollinearity. Which action should be taken to address this issue?

Accepted Answer

Remove one of the two features. Removing one of the highly correlated features reduces multicollinearity without losing much information, as they are nearly linearly dependent. Standardization does not fix multicollinearity. PCA would reduce dimensionality but may harm interpretability. Keeping both can destabilize coefficient estimates.

Answer

Apply PCA to reduce dimensionality to a single component

Answer

Keep both features as they are because linear models are robust to multicollinearity

Answer

Standardize both features using StandardScaler

Question 8

Which THREE components are required to set up automated model retraining in response to performance degradation using Amazon SageMaker? (Select THREE.)

Accepted Answer

A CloudWatch alarm that triggers when a quality metric falls below a threshold.. Option B is correct because a CloudWatch alarm monitoring a model quality metric (e.g., MAE, F1) and firing when it breaches a threshold is the detection mechanism that signals performance degradation and can invoke an action. Option C is correct because SageMaker Model Monitor (specifically a monitoring schedule with a quality baseline) captures inference data from the endpoint and computes the quality metrics that CloudWatch then evaluates. Option D is correct because an AWS Lambda function is the automation glue that, when invoked by the CloudWatch alarm (via SNS or EventBridge), calls the SageMaker API to start a training job or pipeline execution for retraining. Option A is not required because manual approval via an SNS email subscription is a human-in-the-loop step, not a component of automated retraining. Option E is not required because a canary traffic-shift production variant is a deployment strategy for releasing a new model, not a prerequisite for triggering retraining.

Answer

An Amazon SNS topic with a subscription to send a manual approval email.

Answer

A production variant with a canary traffic shift configuration.

Question 9

A company uses SageMaker Pipelines to automate their ML workflow. They need to add model versioning and approval workflow. Which THREE steps should they include in their pipeline to achieve this? (Choose THREE.)

Accepted Answer

RegisterModel step. Option A (RegisterModel step) is correct because it creates a model package in the SageMaker Model Registry, which is the core mechanism for versioning models and tracking their approval status. Option C (Condition step) is correct because it enables the pipeline to branch based on evaluation results or approval status, allowing the workflow to proceed only when the model meets criteria or is approved. Option D (Processing step for evaluation) is correct because it runs the evaluation logic that produces the metrics used by the Condition step to decide whether the model should be registered or approved. Option B (Training step) is not one of the three required steps here since training alone does not provide versioning or approval; it is typically a prerequisite but not the mechanism for versioning/approval. Option E (Transform step) is not correct because batch transform is for inference, not for model versioning or approval workflow.

Answer

Training step

Answer

Transform step

Question 10

A data scientist needs to create a feature group in Amazon SageMaker Feature Store for real-time recommendations. Which TWO configurations are required? (Select TWO.)

Accepted Answer

Specify a record identifier feature. Online store must be enabled for real-time serving, and a record identifier is required to uniquely identify records. Offline store and feature description are optional; time-to-live is not a standard feature.

Answer

Enable the offline store

Answer

Provide a feature description

Answer

Set a time-to-live (TTL) for the records

Question 11

A machine learning engineer observes that model performance on a SageMaker endpoint has degraded over the past week. Ground truth labels are available with a 2-day delay. The engineer wants to automatically trigger a retraining pipeline when prediction quality drops below an acceptable threshold. Which approach is most appropriate?

Accepted Answer

Use SageMaker Model Monitor - Model Quality Monitor with ground truth, create a CloudWatch alarm on the metric, and trigger an AWS Lambda function to start retraining. SageMaker Model Monitor's Model Quality Monitor is specifically designed to compare model predictions against ground truth labels (available with a 2-day delay) and track metrics like accuracy, precision, recall, or F1 score. You can configure a CloudWatch alarm on a metric such as 'accuracy' dropping below a threshold, which triggers an AWS Lambda function to start the retraining pipeline. This automates the detection of prediction quality degradation and the retraining response without manual intervention.

Answer

Manually evaluate the model weekly and retrain as needed

Answer

Use SageMaker Model Monitor - Data Quality Monitor to detect drift, then trigger retraining

Answer

Use SageMaker Clarify to monitor bias drift and trigger retraining

Question 12

A data engineer needs to perform feature selection on a dataset with 500 numeric features to train a regression model. The engineer wants to remove features that are redundant or have low predictive power. Which TWO techniques should the engineer consider? (Select TWO.)

Accepted Answer

Correlation analysis. Correlation analysis (D) is correct because it identifies pairs of numeric features whose values move together, letting the engineer drop redundant, highly correlated predictors before training the regression model. Lasso regularization (E) is correct because its L1 penalty shrinks the coefficients of weak or irrelevant features exactly to zero, effectively performing embedded feature selection on the 500 numeric inputs. Oversampling (A) is a class-imbalance technique that duplicates or synthesizes minority-class samples and does nothing to remove redundant or low-predictive-power features. Standardization (B) merely rescales features to comparable ranges and does not eliminate any feature. One-hot encoding (C) is a categorical-variable transformation that expands categories into binary columns and is irrelevant to a dataset of numeric features.

Answer

Oversampling

Answer

Standardization

Answer

One-hot encoding

Question 13

An ML engineer monitors a SageMaker endpoint for data drift. They set up SageMaker Model Monitor to compare inference data against a baseline created from the training dataset. The monitoring schedule runs daily and reports violations. Which monitoring type should be configured to detect if the distribution of a numerical feature in real-time inference data differs significantly from the training distribution?

Accepted Answer

Data quality monitoring. SageMaker Model Monitor's data quality monitoring detects feature distribution drift (statistical drift) between baseline and live data. Model quality monitoring requires ground truth labels, bias drift monitors fairness metrics, and feature attribution drift monitors SHAP values.

Answer

Feature attribution drift monitoring

Answer

Bias drift monitoring

Answer

Model quality monitoring

Question 14

A machine learning engineer wants to store, share, and manage features for multiple ML models across an organization. The features need to be accessible for both real-time inference (low-latency) and batch training. Which AWS service should the engineer use?

Accepted Answer

Amazon SageMaker Feature Store. Amazon SageMaker Feature Store is purpose-built for storing, sharing, and managing ML features across teams and models. It provides a unified feature store with both an online store (backed by Amazon DynamoDB or Redis) for low-latency real-time inference and an offline store (backed by Amazon S3) for batch training, directly addressing the requirement for dual access patterns.

Answer

Amazon S3 with AWS Glue Data Catalog

Answer

Amazon Redshift

Answer

Amazon DynamoDB

Question 15

A data scientist is building a model to predict customer churn based on historical data. The dataset has 10 features and 100,000 records, and the target is binary. Which algorithm is most appropriate for this binary classification problem?

Accepted Answer

Logistic regression. Logistic regression is the most appropriate algorithm for this binary classification problem because it directly models the probability of the binary target variable using a logistic (sigmoid) function, making it a natural fit for predicting customer churn (yes/no). It is efficient with 100,000 records and 10 features, providing interpretable coefficients that indicate feature importance, which is crucial for understanding churn drivers.

Answer

Principal component analysis

Answer

K-means clustering

Answer

Linear regression