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| Section | Weight | Objectives |
|---|---|---|
| Model Deployment and Inference | 20% | - Inference optimization (latency, throughput) - SageMaker endpoints configuration - A/B testing and shadow mode deployment - Model deployment strategies (real-time, batch) - Model versioning and rollback |
| Model Development | 30% | - Transfer learning and fine-tuning - Distributed training - Training and validation strategies - Hyperparameter optimization - Algorithm selection and model architecture - ML frameworks (SageMaker, built-in algorithms) |
| Data Processing | 22% | - Data validation and quality assessment - Data ingestion and transformation - Handling imbalanced data - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Data preprocessing and feature engineering |
| MLOps and Monitoring | 28% | - CI/CD pipelines for ML - Model lineage and reproducibility - Cost optimization for ML workloads - Incident response and remediation - Model monitoring and drift detection - Security and access management for ML |
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NEW QUESTION # 61
An ML engineer wants an Amazon SageMaker notebook to automatically stop running after
1 hour of idle time. How can the ML engineer accomplish this goal?
Answer: A
Explanation:
The correct approach is to use a SageMaker lifecycle configuration and place the auto-stop-idle script in the Start Notebook section. This ensures the notebook runs the monitoring script on startup, which checks for idle time and automatically stops the notebook after the defined threshold (1 hour in this case).
NEW QUESTION # 62
An ML engineer is building a logistic regression model to predict customer churn for subscription services.
The dataset contains two string variables: location and job_seniority_level.
The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values.
The ML engineer must perform preprocessing on the variables.
Which solution will meet this requirement?
Answer: A
Explanation:
Logistic regression requires numeric input features and is sensitive to how categorical variables are encoded.
AWS feature engineering best practices recommend one-hot encoding for low-cardinality categorical variables with no inherent order and ordinal encoding for categorical variables with a meaningful order.
The location feature has only three distinct values and no ordinal relationship, making one-hot encoding the most appropriate method. This prevents the model from inferring a false numerical relationship between locations.
The job_seniority_level feature typically has an inherent order (for example: junior, mid-level, senior, lead).
Even with more than 10 categories, ordinal encoding preserves this natural hierarchy while keeping the feature dimensionality manageable.
Tokenization is used for unstructured text, not structured categorical variables. Standard scaling applies only to continuous numeric features and is not suitable for categorical string variables.
AWS documentation explicitly highlights using one-hot encoding for nominal features and ordinal encoding for ordered categorical features when preparing data for linear models such as logistic regression.
Therefore, Option B is the correct and AWS-aligned solution.
NEW QUESTION # 63
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?
Answer: A
Explanation:
When running consecutive training jobs in Amazon SageMaker, infrastructure provisioning can introduce latency, as each job typically requires the allocation and setup of compute resources. To minimize this startup time and enhance efficiency, Amazon SageMaker offers Managed Warm Pools.
Key Features of Managed Warm Pools:
* Reduced Latency: Reusing existing infrastructure significantly reduces startup time for training jobs.
* Configurable Retention Period: Allows retention of resources after training jobs complete, defined by the KeepAlivePeriodInSeconds parameter.
* Automatic Matching: Subsequent jobs with matching configurations (e.g., instance type) can reuse retained infrastructure.
Implementation Steps:
* Request Warm Pool Quota Increase: Increase the default resource quota for warm pools through AWS Service Quotas.
* Configure Training Jobs:
* Set KeepAlivePeriodInSeconds for the first training job to retain resources.
* Ensure subsequent jobs match the retained pool's configuration to enable reuse.
* Monitor Warm Pool Usage: Track warm pool status through the SageMaker console or API to confirm resource reuse.
Considerations:
* Billing: Resources in warm pools are billable during the retention period.
* Matching Requirements: Jobs must have consistent configurations to use warm pools effectively.
Alternative Options:
* Managed Spot Training: Reduces costs by using spare capacity but doesn't address startup latency.
* SageMaker Training Compiler: Optimizes training time but not infrastructure setup.
* SageMaker Distributed Data Parallelism Library: Enhances training efficiency but doesn't reduce setup time.
By using Managed Warm Pools, the company can significantly reduce startup latency for consecutive training jobs, ensuring faster experimentation cycles with minimal operational overhead.
AWS Documentation: Managed Warm Pools
AWS Blog: Reduce ML Model Training Job Startup Time
NEW QUESTION # 64
A company wants to use large language models (LLMs) that are supported by Amazon Bedrock to develop a chat interface for the company ' s internal technical documentation. The company stores the documentation as dozens of text files that are several megabytes in total size. The company updates the text files often.
Which solution will meet these requirements MOST cost-effectively?
Answer: D
Explanation:
Option D is correct because Amazon Bedrock Knowledge Bases are designed for applications that need to answer questions using private documents without retraining or repeatedly fine-tuning a foundation model.
AWS documentation states that with Amazon Bedrock Knowledge Bases , you can build applications enriched by context retrieved from a knowledge base, and that this provides an out-of-the-box RAG solution
. AWS also explicitly says that adding a knowledge base increases cost-effectiveness by removing the need to continually train your model to use your private data. That matches this use case very closely.
The question also says the documentation consists of text files that are only several megabytes total and are updated often . A retrieval-based approach is more economical and operationally simpler than creating a new model or repeatedly fine-tuning one whenever the documents change. AWS documentation for Bedrock knowledge bases describes adding data sources and running ingestion jobs to process and index the content, which is exactly the pattern needed for frequently updated internal documentation used by a chat interface.
The other options are not as cost-effective. Creating a new LLM is far beyond the need here. Guardrails help control model behavior and policy enforcement, but they do not serve as a document retrieval layer for internal documentation. Fine-tuning a model on frequently changing text files is usually more expensive and less flexible than using retrieval augmentation. For a modest-sized, frequently updated documentation corpus, the AWS-native and most cost-effective solution is to load the files into an Amazon Bedrock knowledge base and use it to provide context at inference time. Therefore, the best verified answer is D .
NEW QUESTION # 65
A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.
Which solution will meet this requirement?
Answer: C
Explanation:
AWS Secrets Manager is designed for securely storing, managing, and automatically rotating secrets, including API tokens. By configuring a Lambda function for custom rotation logic, the solution can automatically rotate the API tokens every 3 months as required. Secrets Manager simplifies secret management and integrates seamlessly with other AWS services, making it the ideal choice for this use case.
NEW QUESTION # 66
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