MLA-C01 Practice Test 6 — 10 Questions

Question 1

A financial services company is deploying a fraud detection model on SageMaker. To comply with regulations, they must ensure that the model's predictions are not biased against protected groups. They plan to monitor bias drift post-deployment using SageMaker Clarify. Which data inputs are required to configure Clarify's bias drift monitoring?

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

Accepted Answer

Baseline training data with ground truth labels and inference data with predictions. SageMaker Clarify's bias drift monitoring requires a baseline—specifically, the training data with ground truth labels—to establish the original bias metrics, and the inference data with predictions to compute post-deployment bias metrics. By comparing these two datasets, Clarify detects statistically significant shifts in bias over time, which is essential for regulatory compliance in fraud detection models.

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Only the inference data with predictions

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Only the ground truth labels for recent predictions

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Only the training data with feature attributions

Question 2

A team uses SageMaker ML Lineage Tracking to capture the metadata of their ML workflow. They want to query the lineage to see which model version was trained from a specific dataset. Which Lineage Tracking entity represents the dataset?

Accepted Answer

Artifact. In SageMaker ML Lineage Tracking, an Artifact represents a tangible object or data — datasets, models, model versions, and endpoints are all artifacts. The dataset is therefore represented as an Artifact, and lineage queries traverse associations between artifacts, actions, and contexts to trace which model version was trained from it.

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Association

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Action

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Context

Question 3

A data engineer is building a feature store using Amazon SageMaker Feature Store. The team needs to store features that are updated frequently and require low-latency retrieval for real-time inference. Which type of store should the engineer use?

Accepted Answer

Online store. The online store in Amazon SageMaker Feature Store is designed for low-latency, real-time retrieval of feature values for inference. It is backed by a low-latency storage layer and supports single-digit millisecond reads, which is exactly what real-time inference requires. The offline store, by contrast, is optimized for batch training and historical lookups, not real-time serving.

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Both online and offline store

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Offline store

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Amazon DynamoDB directly

Question 4

A company is using SageMaker to serve a model for real-time predictions. They want to test a new model version by routing a small percentage of live traffic to it while the rest goes to the current model. They also need to compare performance metrics. Which TWO actions should they take? (Select TWO.)

Accepted Answer

Monitor the performance of both variants using SageMaker CloudWatch metrics. Amazon CloudWatch provides built-in metrics for SageMaker endpoints, including latency, invocation counts, and error rates, which can be monitored per production variant. This allows the company to compare the performance of the new model version against the current model in real time. Option E is correct because SageMaker endpoints support multiple production variants, and you can set an initial traffic weight (e.g., 5%) to route a small percentage of live traffic to the new model while the rest goes to the existing variant.

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Deploy the new model to a separate endpoint and use Route 53 to split traffic

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Compile the new model with SageMaker Neo before deployment

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Use SageMaker Batch Transform to evaluate the new model

Question 5

A data science team needs to deploy a trained PyTorch model for real-time inference with sub-100ms latency. The model fits on a single GPU. Which SageMaker inference option is MOST cost-effective while meeting the latency requirement?

Accepted Answer

SageMaker real-time endpoint on ml.g4dn.xlarge. SageMaker real-time endpoints provide dedicated, persistent instances that can handle synchronous inference with sub-100ms latency. The ml.g4dn.xlarge instance includes a single NVIDIA T4 GPU, which is sufficient for the model size and offers the lowest cost among GPU instances that meet the latency requirement. This option balances performance and cost for real-time, low-latency inference.

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SageMaker Batch Transform

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SageMaker Async Inference

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SageMaker Serverless Inference

Question 6

An ML team uses Amazon SageMaker Data Wrangler to prepare a dataset for a binary classification model. They suspect the dataset might contain bias against a certain demographic group. They want to detect and visualize potential bias before training the model. Which feature of SageMaker should they use?

Accepted Answer

SageMaker Clarify. SageMaker Clarify is the purpose-built feature for detecting bias in datasets and models, providing pre-training bias metrics (e.g., class imbalance, disparate impact) and post-training metrics, plus visualizations in SageMaker Studio. It analyzes the dataset before training to surface potential bias against demographic groups, exactly matching the team's requirement.

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SageMaker Debugger

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SageMaker Experiments

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SageMaker Model Monitor

Question 7

A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?

Accepted Answer

Real-time endpoint on ml.g4dn instances. Real-time endpoints on GPU instances (ml.g4dn) provide low latency and GPU acceleration, ideal for high-traffic, latency-sensitive workloads.

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Real-time endpoint on ml.m5 instances

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Batch Transform

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Serverless Inference

Question 8

A data scientist is preparing text data for sentiment analysis. They need to convert the text into numerical features while reducing the impact of common words. Which feature extraction method should they use?

Accepted Answer

TF-IDF vectorization. TF-IDF (Term Frequency–Inverse Document Frequency) converts text to numerical features while down-weighting common words (like 'the', 'is') that appear across many documents, exactly matching the requirement to reduce the impact of common words. It is the standard feature extraction method for sentiment analysis when common-word influence must be minimized.

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Word2Vec embeddings

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Label encoding of each word

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CountVectorizer with n-grams

Question 9

A machine learning team has a model that needs to serve predictions with very low latency (under 10 ms) for a real-time web application. The model is a small ensemble of three neural networks that fits in memory. Which SageMaker inference option is MOST appropriate?

Accepted Answer

SageMaker real-time endpoint. SageMaker real-time endpoints are designed for low-latency, synchronous inference, making them the best fit for a model that must serve predictions in under 10 ms. Since the ensemble of three neural networks fits in memory, a real-time endpoint can keep the model loaded and respond to each request with minimal overhead, typically using HTTPS and the SageMaker InvokeEndpoint API.

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SageMaker batch transform

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SageMaker asynchronous inference

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SageMaker serverless inference

Question 10

An ML team is using Amazon SageMaker Feature Store to serve features for both real-time inference and batch training. They need to ensure that training data uses feature values as they were at the time of each event. Which type of query should they use?

Accepted Answer

Point-in-time query from the offline store. SageMaker Feature Store's offline store supports point-in-time queries, which return feature values as they existed at a specified event timestamp for each record. This prevents 'feature leakage' — using future feature values that were not available when the event occurred — which would otherwise inflate training accuracy and break production parity.

Answer

Full table scan of the offline store

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Join query across both stores

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Latest record query from the online store