MLA-C01 Practice Test 27 — 15 Questions

Question 1

A data scientist wants to track feature definitions, share them across teams, and serve features for both training and real-time inference. Which AWS service provides these capabilities?

Accepted Answer

Amazon SageMaker Feature Store. Amazon SageMaker Feature Store is purpose-built for ML workflows, providing a centralized repository to define, share, and serve features for both training (batch) and real-time inference (low-latency retrieval). It supports offline and online stores, enabling consistent feature definitions across teams and automatic feature ingestion via SageMaker Pipelines or custom code.

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Amazon DynamoDB

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Amazon S3

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AWS Glue Data Catalog

Question 2

A machine learning engineer needs to automatically retrain a model whenever SageMaker Model Monitor detects data drift. Which combination of services should be used to trigger the retraining pipeline?

Accepted Answer

CloudWatch Alarms → SNS → Lambda → SageMaker Processing job. SageMaker Model Monitor publishes metrics to CloudWatch, and when data drift is detected, a CloudWatch Alarm triggers an SNS notification, which invokes a Lambda function to start a SageMaker Processing job for retraining. This architecture decouples monitoring from retraining and uses native AWS services for event-driven automation.

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SageMaker Ground Truth → Lambda → SageMaker Training job

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SageMaker Model Monitor → EventBridge → Step Functions → SageMaker Training job

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SageMaker Data Wrangler → SNS → SageMaker Training job

Question 3

A company uses AWS Glue ETL jobs to process data from multiple sources and store it in a centralized S3 data lake for ML. They want to ensure that schema changes in source tables are automatically updated in the Glue Data Catalog without manual intervention. Which feature should they enable?

Accepted Answer

Use AWS Glue crawlers to update the Data Catalog. AWS Glue crawlers can be configured to automatically detect and apply schema changes from source data stores to the Glue Data Catalog. When a crawler runs, it infers the schema of the data and updates the catalog tables accordingly, enabling schema evolution without manual intervention. This directly addresses the requirement for automatic updates when source table schemas change.

Answer

Use AWS Lambda to manually update the Data Catalog

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Enable Amazon S3 event notifications for schema changes

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Enable AWS Glue Data Catalog schema auto-update

Question 4

A machine learning team deploys a fraud detection model on a SageMaker endpoint. The model's predictions are used in real-time. The team wants to monitor for data drift by comparing incoming data distributions against a baseline created from the training data. Which SageMaker capability should they use?

Accepted Answer

SageMaker Model Monitor - Data Quality Monitor. SageMaker Model Monitor's Data Quality Monitor is specifically designed to detect data drift by comparing the statistical distribution of incoming inference data against a baseline computed from the training dataset. This capability tracks metrics like mean, variance, and quantiles for each feature, alerting when significant deviations occur. For a fraud detection model requiring real-time monitoring of input distributions, this is the correct choice.

Answer

SageMaker Model Monitor - Model Quality Monitor

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SageMaker Model Monitor - Feature Attribution Drift Monitor

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SageMaker Model Monitor - Bias Drift Monitor

Question 5

A company needs to serve real-time predictions from a large ensemble of three deep learning models, each requiring different inference environments (PyTorch, TensorFlow, MXNet). Which SageMaker endpoint type supports running multiple inference containers together?

Accepted Answer

Multi-container endpoint. Amazon SageMaker multi-container endpoints allow you to run multiple inference containers (e.g., PyTorch, TensorFlow, MXNet) within a single endpoint, each handling different models or inference environments. This is achieved by deploying multiple containers behind a single endpoint with a serial or direct invocation pattern, enabling real-time predictions from the ensemble without managing separate endpoints.

Answer

Multi-model endpoint

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Real-time endpoint with a single container

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Asynchronous endpoint

Question 6

A company uses SageMaker Ground Truth to label a dataset for object detection. They set up a labeling job with a private workforce. After labeling, they export the dataset and train a model using SageMaker's built-in object detection algorithm. The model achieves high accuracy on the test set but low accuracy on a small holdout set that was manually labeled by an expert. What might be the issue?

Accepted Answer

The labeling job had insufficient worker consensus.. Low worker consensus in a Ground Truth labeling job indicates inconsistent annotations among workers, leading to noisy labels. When the model trains on these inconsistent labels, it learns patterns that may not generalize to a clean, expert-labeled holdout set, causing a significant accuracy drop despite high performance on the test set (which likely shares the same labeling noise). Ground Truth uses a 'consensus' mechanism to finalize labels, and insufficient consensus means the final labels may be unreliable for training a robust object detection model.

Answer

The dataset size is too small.

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The object detection algorithm is not suitable.

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The holdout set uses a different labeling schema.

Question 7

A company uses SageMaker Studio and wants to restrict studio user access to only the VPC. They also need to encrypt the data exchanged between the Studio app and the kernel gateway. Which configuration should they apply?

Accepted Answer

Enable VPC-only mode and inter-container traffic encryption. Enabling VPC-only mode restricts all SageMaker Studio traffic to the VPC, preventing any internet-bound communication, while inter-container traffic encryption ensures that data exchanged between the Studio app and the kernel gateway is encrypted in transit using TLS. This combination satisfies both requirements: network confinement to the VPC and encryption of inter-component traffic.

Answer

Configure Studio to run in a private subnet and disable internet access

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Use a VPC endpoint for SageMaker and enable KMS encryption

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Enable network isolation and use a VPC with a NAT gateway

Question 8

A company wants to reduce costs for a real-time inference endpoint that experiences predictable traffic spikes during business hours and low traffic at night. Which auto-scaling policy is MOST cost-effective while maintaining performance?

Accepted Answer

Scheduled scaling that increases instances before business hours and decreases after. Scheduled scaling directly aligns capacity with the predictable traffic pattern (business hours vs. night), allowing you to proactively add instances before demand increases and remove them afterward. This avoids the cost of over-provisioning during low-traffic periods and the latency of reactive scaling, making it the most cost-effective approach for a known, recurring schedule.

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Step scaling based on CPU utilization

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Manual scaling by the operations team

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Target tracking with a custom metric for response time

Question 9

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.

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Amazon S3 with AWS Glue Data Catalog

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Amazon Redshift

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Amazon DynamoDB

Question 10

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. They need to identify potential bias in the data before training. Which SageMaker feature should they use?

Accepted Answer

Amazon SageMaker Clarify. Amazon SageMaker Clarify is the correct feature because it is specifically designed to detect bias in datasets and machine learning models. It provides built-in bias metrics (e.g., pre-training bias) and can generate bias reports during data preparation, directly addressing the need to identify potential bias before training.

Answer

Amazon SageMaker Model Monitor

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Amazon SageMaker Debugger

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Amazon SageMaker Pipelines

Question 11

An ML engineer is debugging a training job that is consistently failing due to an out-of-memory error. The engineer is using SageMaker's built-in XGBoost algorithm. Which Debugger rule can help identify the issue?

Accepted Answer

Exploding gradients. The 'Exploding gradients' rule detects when gradients become too large, which is a common cause of training instability but not necessarily OOM. The 'Overfit' rule detects overfitting. The 'Dead relu' rule is for ReLU activation. None of these directly address OOM. However, Debugger does not have a specific OOM rule; instead, the engineer should monitor memory utilization via CloudWatch or adjust instance type. Among the options, 'Exploding gradients' is the most relevant because large gradients can lead to memory spikes.

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Overfit

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Dead relu

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OOM rule

Question 12

A financial services firm is training a fraud detection model using SageMaker. The dataset is highly imbalanced (0.1% fraudulent transactions). The model currently achieves 99.9% accuracy but only catches 5% of fraud cases. Which metric should the team prioritize to evaluate model performance?

Accepted Answer

Recall. Recall (true positive rate) measures the proportion of actual positives correctly identified. For fraud detection, catching fraud is critical; accuracy is misleading due to class imbalance.

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Accuracy

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Precision

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F1-score

Question 13

A machine learning engineer is designing a SageMaker Pipeline that includes a training step, a processing step for evaluation, and a condition step to decide whether to register the model. The pipeline should support caching to avoid redundant runs when inputs haven't changed. Which three steps must have caching enabled? (Select THREE.)

Accepted Answer

Training step. For caching to avoid redundant runs, the steps that produce outputs that can be reused must have caching enabled. The processing step (evaluation) and training step both generate outputs that can be cached if their inputs (code, data, hyperparameters) remain the same. The condition step does not produce outputs to cache; it just branches. The RegisterModel step typically registers metadata, but its inputs (model artifact, metrics) may be generated by previous steps; enabling caching on the RegisterModel step can also avoid re-running if the same model artifact is already registered.

Answer

Transform step (if used)

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Condition step

Question 14

A company has deployed a machine learning model on Amazon SageMaker and wants to automatically detect when the distribution of input features deviates significantly from the training data distribution. Which SageMaker feature should they use?

Accepted Answer

SageMaker Model Monitor – Data Quality Monitoring. SageMaker Model Monitor – Data Quality Monitoring is the correct choice because it is specifically designed to detect deviations in the distribution of input features compared to the training data distribution. It continuously monitors incoming inference requests and compares statistical properties (e.g., mean, variance, or histogram) against a baseline computed from the training dataset, alerting when drift is detected.

Answer

SageMaker Clarify

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SageMaker Edge Manager

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SageMaker Model Monitor – Model Quality Monitoring

Question 15

A data scientist is using Amazon SageMaker Data Wrangler to prepare a dataset. The dataset contains a column with date strings in the format 'YYYY-MM-DD'. The data scientist wants to extract the year, month, and day as separate features. Which Data Wrangler transform should be used?

Accepted Answer

Parse date transform.. The 'Parse date' transform in Amazon SageMaker Data Wrangler is specifically designed to convert date strings into structured datetime components. By applying this transform to the 'YYYY-MM-DD' column, the data scientist can automatically extract year, month, and day as separate features, enabling downstream feature engineering without manual string parsing.

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Encode categorical transform.

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Scale values transform.

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Handle missing transform.