Free Amazon Web Services MLA-C01 Practice Exam with Questions & Answers | Set: 2

SageMaker script mode allows ML engineers to bring existing training scripts written for frameworks such as PyTorch and TensorFlow directly into SageMaker with minimal changes. AWS documentation explicitly states that script mode is designed to support migration of existing ML workloads.

With script mode, the user provides a custom training script, and SageMaker handles infrastructure provisioning, distributed training, logging, and model artifact storage. This makes script mode ideal for companies that want to reuse established codebases without rewriting them.

Built-in algorithms require adopting AWS-provided implementations. SageMaker Canvas is a no-code tool, and JumpStart provides pretrained models and templates but does not focus on custom script reuse.

Therefore, Option D is the correct and AWS-recommended choice.

Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.

Amazon Q Business provides built-in guardrails to control and constrain model responses. One such control is the ability to define blocked phrases, which explicitly prevents specified terms from appearing in generated responses.

Configuring the competitor’s name as a blocked phrase guarantees that Amazon Q Business will never include that name in responses, fully meeting the requirement. This approach is deterministic and does not rely on ranking or retrieval behavior.

Option B is incorrect because retrievers control what documents are fetched, not how responses are generated. Option C adds unnecessary complexity and does not guarantee exclusion in generated answers. Option D only deprioritizes content and does not prevent it from appearing.

Therefore, blocking the competitor’s name is the correct solution.

AWS Glue Data Quality provides managed, declarative data quality checks with minimal configuration. Combined with Glue crawlers, it enables automatic schema discovery and quality validation without custom code.

Option A uses native AWS services designed for this exact purpose, minimizing operational overhead. Options B and C require custom code and maintenance. Option D is not designed for data validation.

AWS documentation explicitly recommends Glue Data Quality rules for scalable, automated data quality checks in analytics pipelines.

Therefore, Option A is the correct and AWS-aligned solution.

SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model ' s predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.

Amazon SageMaker multi-model endpoints (MME) are designed to host multiple models behind a single endpoint, dynamically loading models into memory on demand. AWS documentation explicitly recommends MME as the most cost-effective solution when multiple models share the same ML framework and inference container.

With MME, SageMaker loads models from Amazon S3 only when they are invoked and unloads idle models automatically. This dramatically reduces the number of instances required and avoids paying for always-on resources for infrequently used models.

Multi-container endpoints are intended for inference pipelines or ensembles and require all containers to be loaded at startup, which increases cost. Deploying separate endpoints for each model results in the highest cost due to duplicated infrastructure.

AWS best practices clearly position multi-model endpoints as the optimal choice for reducing inference costs when hosting many models with similar runtime requirements.

Therefore, Option B is the correct and AWS-verified solution.

Option D is correct because Amazon SageMaker Model Registry is the AWS feature specifically designed to catalog models, manage model versions, and organize them into model groups. AWS documentation states that you can create a Model Group and then register each model you train, and the Model Registry adds it to the Model Group as a new model version. That directly matches the requirement to automatically create versions of ML models as models are created.

The AWS docs also describe a standard workflow in which you first create a Model Group, then create an ML pipeline, and for each run of the ML pipeline, create a model version that is registered in that group. This means Model Registry supports exactly the kind of lifecycle management and version tracking needed in an MLOps workflow where new model artifacts are produced over time and need consistent governance.

The other options do not meet the requirement. Amazon ECR stores container images, not model version history. SageMaker Marketplace model packages are for discovering or subscribing to third-party or prebuilt solutions, not for automatically versioning your organization’s trained models. SageMaker ML Lineage Tracking helps trace artifacts, datasets, and processing steps for reproducibility, but AWS documentation distinguishes lineage from the actual model version management capability provided by Model Registry. Model Registry is the AWS-native feature for keeping versioned records of trained models and managing approval status, deployment readiness, and metadata in a centralized way.

Therefore, the fully verified AWS-docs answer is D, because SageMaker Model Registry is the service purpose-built to create and manage model versions as new models are produced.

In Amazon SageMaker AI, identifying bias in machine learning datasets before model training is a critical step to ensure fairness and reliability of predictions. This process is referred to as pre-training bias analysis, and it focuses on understanding whether the training data itself introduces bias—particularly through imbalanced class labels or sensitive attributes.

The Difference in Proportions of Labels (DPL) is a pre-training bias metric specifically designed to measure class imbalance. DPL compares the proportion of a specific label (such as a positive outcome) across different groups or classes within a dataset. If one class or group is overrepresented relative to another, the DPL value will deviate significantly from zero, clearly indicating imbalance. AWS documentation highlights DPL as a key metric used by SageMaker Clarify to detect label imbalance prior to model training.

By contrast, Mean Squared Error (MSE) is a regression evaluation metric used after model training to measure prediction error, not dataset bias. Silhouette score is an unsupervised learning metric used to evaluate clustering quality, making it irrelevant for supervised classification bias detection. Structural Similarity Index Measure (SSIM) is an image-quality metric used in computer vision tasks and has no application in dataset bias analysis.

Using DPL allows ML engineers to proactively detect and address skewed label distributions—such as by re-sampling, re-weighting, or collecting additional data—before training begins. This aligns with AWS best practices for responsible AI and helps reduce the risk of biased predictions that could negatively impact real-world decision-making.

Therefore, Difference in Proportions of Labels (DPL) is the correct and AWS-recommended metric for confirming class imbalance during pre-training bias analysis in Amazon SageMaker AI.

Apache Parquet is a columnar storage file format optimized for complex and large datasets. It provides efficient reading and processing by accessing only the required columns, which reduces I/O and speeds up data handling. This makes it ideal for use with Amazon SageMaker Canvas, where minimizing processing time is important for training ML models. Parquet is also compatible with S3 and widely supported in data analytics and ML workflows.

Option A is correct because Amazon SageMaker AI supports automatic scaling for hosted models, and AWS documentation states that auto scaling dynamically adjusts the number of instances provisioned for a model in response to workload changes. When traffic increases, auto scaling brings more instances online; when traffic decreases, it removes unneeded capacity. This directly matches the requirement to keep the recommendation endpoint available during an expected increase in user traffic.

AWS also documents that SageMaker integrates with Application Auto Scaling, and you can scale SageMaker endpoint variants by using target tracking, step scaling, and scheduled scaling policies. That means the service is purpose-built for this scenario: maintaining availability and capacity as inference demand changes over time. Since the model is already being served from a SageMaker endpoint, the most direct and AWS-recommended solution is to configure endpoint auto scaling rather than redesigning the deployment.

The other options do not address the requirement as effectively. Creating a new endpoint does not automatically handle fluctuating traffic and adds unnecessary operational complexity. SageMaker Neo optimizes models for inference performance on supported hardware, but it does not provide elastic capacity management. Attaching an Auto Scaling group to a SageMaker endpoint is incorrect because SageMaker endpoints do not use EC2 Auto Scaling groups directly; scaling is handled through SageMaker’s integration with Application Auto Scaling. Therefore, to maintain availability during traffic spikes, the best AWS-documented answer is A: configure auto scaling on the SageMaker AI endpoint.