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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation for Machine Learning (ML) | 28% | - Data ingestion and collection
|
| Topic 2: ML Model Development | 26% | - Model selection and training
|
| Topic 3: ML Solution Monitoring, Maintenance, and Security | 24% | - Monitoring and observability
|
| Topic 4: Deployment and Orchestration of ML Workflows | 22% | - ML pipeline orchestration
|
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45. Frage
A bank needs to use Amazon SageMaker AI to create an ML model to determine which customers qualify for a new product. The bank must use algorithms that SageMaker AI directly supports. The model must be explainable to the bank ' s regulators.
Which modeling approach will meet these requirements?
Antwort: B
Begründung:
Option B is correct because the bank needs a SageMaker-supported algorithm and the resulting model must be explainable for regulators. AWS documentation states that the Amazon SageMaker AI linear learner algorithm is a built-in algorithm that provides a solution for classification and regression problems
. Since determining whether customers qualify for a new product is a classification-style decision problem, linear learner fits the business requirement directly.
The explainability requirement strongly favors a simpler and more interpretable model family. AWS Prescriptive Guidance on model interpretability states that simple models are considered more interpretable because the effects of input features on the output are easier to understand. It specifically gives linear regression as an example where the magnitudes of coefficients provide a clear global feature-importance signal. While the question does not explicitly mention SageMaker Clarify, AWS documentation also says SageMaker offers explainability features to help interpret predictions, which complements inherently interpretable model types such as linear models.
The other options are weaker fits. Object2Vec is mainly for learning embeddings and similarity relationships rather than a straightforward explainable eligibility model. A neural network may work technically, but it is generally less interpretable for regulatory review. k-means is an unsupervised clustering algorithm and is not suitable for predicting whether a customer qualifies for a product. Because the bank wants a directly supported SageMaker algorithm and a model that is easier to explain to regulators, the best AWS-aligned answer is B, linear learner .
46. Frage
Case Study
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?
Antwort: D
Begründung:
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 offersManaged 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 usingManaged Warm Pools, the company can significantly reduce startup latency for consecutive training jobs, ensuring faster experimentation cycles with minimal operational overhead.
References:
* AWS Documentation: Managed Warm Pools
* AWS Blog: Reduce ML Model Training Job Startup Time
47. Frage
A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.
Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?
Antwort: B
Begründung:
SageMaker Serverless Inference is ideal for workloads with predictable, intermittent demand. By enabling provisioned concurrency, the model can handle multiple invocations quickly during the high-demand 2-hour period. AWS manages the underlying infrastructure and scaling, ensuring the solution meets performance requirements with minimal operational overhead. This approach is cost-effective since it scales down when not in use.
48. Frage
A company uses AWS CodePipeline to orchestrate a continuous integration and continuous delivery (CI/CD) pipeline for ML models and applications.
Select and order the steps from the following list to describe a CI/CD process for a successful deployment.
Select each step one time. (Select and order FIVE.)
. CodePipeline deploys ML models and applications to production.
CodePipeline detects code changes and starts to build automatically.
. Human approval is provided after testing is successful.
. The company builds and deploys ML models and applications to staging servers for testing.
. The company commits code changes or new training datasets to a Git repository.
Antwort:
Begründung:
Explanation:
Step 1:
The company commits code changes or new training datasets to a Git repository.
This is the trigger point. A source code or data change initiates the CI/CD pipeline.
Step 2:
CodePipeline detects code changes and starts to build automatically.
CodePipeline monitors the Git repository (for example, AWS CodeCommit, GitHub, or Bitbucket) and automatically triggers the pipeline when changes are detected.
Step 3:
The company builds and deploys ML models and applications to staging servers for testing.
The pipeline runs build, training, and test stages (often using AWS CodeBuild and SageMaker) and deploys artifacts to a staging or test environment for validation.
Step 4:
Human approval is provided after testing is successful.
A manual approval action is a best practice for ML workflows to ensure governance, compliance, and quality checks before production deployment.
Step 5:
CodePipeline deploys ML models and applications to production.
After approval, the pipeline automatically deploys the validated model or application to the production environment.
49. Frage
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
* Embedding
* Retrieval Augmented Generation (RAG)
* Temperature
* Token
Antwort:
Begründung:
Explanation:
Text representation of basic units of data processed by LLMs: Token
High-dimensional vectors that contain the semantic meaning of text: Embedding Enrichment of information from additional data sources to improve a generated response: Retrieval Augmented Generation (RAG) Comprehensive Detailed Explanation Token:
Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM processes. For example, " running " might be split into two tokens: " run " and " ing. " Why? Tokens are the fundamental building blocks for LLM input and output processing, ensuring that the model can understand and generate text efficiently.
Embedding:
Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are representations of words, sentences, or even paragraphs in a way that reflects their relationships and meaning.
Why? Embeddings are essential for enabling similarity search, clustering, or any task requiring semantic understanding. They allow the model to " understand " text contextually.
Retrieval Augmented Generation (RAG):
Description: A technique where information is enriched or retrieved from external data sources (e.g., knowledge bases or document stores) to improve the accuracy and relevance of a model ' s generated responses.
Why? RAG enhances the generative capabilities of LLMs by grounding their responses in factual and up-to- date information, reducing hallucinations in generated text.
By matching these terms to their respective descriptions, the ML engineer can effectively leverage these concepts to build robust and contextually aware generative AI applications on Amazon Bedrock.
50. Frage
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