MLA-C01 Practice Questions 2 — 50 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.

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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

Question 11

A team needs to deploy a new model version to production while minimizing risk. They want to route 5% of live traffic to the new model and 95% to the current model, and then gradually increase the new model's traffic. Which SageMaker deployment pattern should they use?

Accepted Answer

Canary deployment using production variants. Canary deployment using production variants in Amazon SageMaker allows you to route a small percentage of live traffic (e.g., 5%) to the new model version while the rest goes to the existing model. You can then gradually increase the traffic to the new model as confidence grows, minimizing risk. This pattern is specifically designed for progressive rollouts with the ability to roll back if issues arise.

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Shadow testing

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Blue/green deployment

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A/B testing with production variants

Question 12

A team uses SageMaker Pipelines for CI/CD. The training step fails due to insufficient memory. How to fix without rewriting code?

Accepted Answer

Increase the instance type in the pipeline step configuration. SageMaker Pipelines allows you to specify the instance type for each training step in the pipeline definition. By increasing the instance type (e.g., from ml.m5.large to ml.m5.xlarge or a memory-optimized instance like ml.r5.large), you allocate more memory to the training container without modifying the training script or algorithm. This directly resolves the out-of-memory error while preserving the existing code.

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Modify the training algorithm to use less memory

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Reduce the batch size in the training script

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Enable managed spot training

Question 13

A company ingests streaming transaction data from multiple sources using Amazon Kinesis Data Streams. The data must be transformed (e.g., JSON parsing, data type conversions) and then stored in Amazon S3 for ML training. The transformation logic may change over time. Which approach provides the greatest flexibility and ease of maintenance?

Accepted Answer

Use Kinesis Data Firehose with a Lambda function for transformation and delivery to S3.. Kinesis Data Firehose with a Lambda function for transformation gives you a fully managed delivery pipeline to S3 while letting you change transformation logic by updating the Lambda code, with no servers to manage. This satisfies both the flexibility (logic can evolve) and ease-of-maintenance requirements.

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Use a custom application running on Amazon EC2 with the Kinesis Client Library to transform and write to S3.

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Use Kinesis Data Analytics with SQL queries to transform data and output to S3.

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Use Kinesis Data Streams directly with a Lambda consumer to transform and store in S3.

Question 14

A machine learning team is building a fraud detection model. They have a dataset with a categorical feature 'merchant_id' that has over 10,000 unique values. Which feature engineering technique should they apply to 'merchant_id' to reduce dimensionality while retaining predictive power?

Accepted Answer

Target encoding. Target encoding replaces each merchant_id category with a statistic derived from the target variable (e.g., mean fraud rate per merchant), collapsing 10,000+ categories into a single numeric column while preserving the category's relationship to the label. This dramatically reduces dimensionality compared to one-hot encoding, which would create over 10,000 sparse columns. Because the encoded value carries predictive signal about fraud, target encoding retains useful information that label or ordinal encoding would lose.

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One-hot encoding

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Ordinal encoding

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Label encoding

Question 15

A data scientist needs to split a time-series dataset into training and testing sets while avoiding data leakage from future values. Which splitting technique should the data scientist use?

Accepted Answer

Time-series split (walk-forward validation). Time-series split (walk-forward validation) is the correct technique because it preserves the temporal order of observations, ensuring that training data always precedes test data. This prevents data leakage from future values, which is critical for time-series forecasting. Random shuffling or standard cross-validation would mix past and future data, leading to overly optimistic performance estimates.

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Random shuffle followed by a 80/20 split

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K-fold cross-validation with shuffled folds

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Stratified sampling based on the target variable

Question 16

A company uses SageMaker JumpStart to deploy a foundation model for a summarization task. They want to minimize costs while still meeting a latency requirement of under 2 seconds. Which option should they consider?

Accepted Answer

Use SageMaker Inference Recommender to select the cheapest instance that meets latency. SageMaker Inference Recommender runs load tests against your model on various instance types and provides latency and cost metrics. By selecting the cheapest instance that still meets the sub-2-second latency requirement, you directly minimize cost while satisfying the performance constraint. This is the most systematic and cost-effective approach for this scenario.

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Deploy the model on a serverless endpoint

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Enable auto-scaling to handle variable traffic

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Use the largest GPU instance to ensure fast inference

Question 17

A financial services company ingests transaction data from multiple sources into an S3 data lake. They want to use AWS Glue to catalog this data and make it queryable by Amazon Athena. The data schema changes frequently as new sources are added. Which AWS Glue feature should they enable to automatically detect and update the schema?

Accepted Answer

AWS Glue crawlers with schema update policy set to 'UPDATE'. AWS Glue crawlers scan data in S3, infer the schema, and write table definitions to the Glue Data Catalog. By setting the crawler's schema update policy to 'UPDATE', the crawler will modify existing table definitions in the catalog when it detects new columns or changed types, keeping Athena queries working as sources evolve. This is the purpose-built feature for automatically detecting and propagating schema changes.

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AWS Glue DataBrew

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AWS Glue ETL job scheduled to run daily

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Manual schema definition in the AWS Glue Data Catalog

Question 18

A company uses Amazon SageMaker Feature Store to store features for a real-time recommendation model. The feature data is updated continuously, and the model must use the most recent feature values for each user at inference time. Which type of Feature Store should the company use for serving features to the model?

Accepted Answer

Online store. The SageMaker Feature Store online store is optimized for low-latency, real-time reads of the latest feature values, which is exactly what a real-time recommendation model needs at inference time. It stores the most recent record for each entity (e.g., user) in a low-latency store backed by a fast database. Offline store is for batch/historical access, not real-time serving.

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Offline store

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Point-in-time queries

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Both online and offline store

Question 19

A team monitors a production endpoint and notices a sudden increase in 5XXError count. Which of the following is the most likely cause?

Accepted Answer

The model container is out of memory or crashing. A sudden increase in 5XX errors, particularly HTTP 503 or 502, typically indicates that the model container is failing to process requests due to resource exhaustion (e.g., OOM kills) or a crash in the inference process. In a production ML endpoint, such errors often stem from the container running out of memory, leading to the container being terminated by the orchestrator (e.g., Kubernetes OOMKill) or the application crashing internally, which directly causes 5XX responses.

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The endpoint is under-provisioned and requests are throttled

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The input data format has changed

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The model is returning predictions with high latency

Question 20

A data scientist is preparing a dataset for binary classification. The dataset has a target variable with 90% of samples belonging to class 0 and 10% to class 1. Which data splitting strategy should the scientist use to ensure that the training and test sets maintain the same class proportion as the original dataset?

Accepted Answer

Stratified sampling. Stratified sampling preserves the class proportion of the original dataset in each split, which is essential for imbalanced binary classification (90/10). By sampling within each class stratum, the training and test sets both retain roughly 90% class 0 and 10% class 1, preventing a split where the minority class is underrepresented or absent.

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Time-series split

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Simple random split

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k-fold cross-validation