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Amazon MLA-C01 Prüfungsplan:

Thema	Einzelheiten
Thema 1	ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Thema 2	Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.
Thema 3	Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Thema 4	ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.

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Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Prüfungsfragen mit Lösungen (Q232-Q237):

232. Frage
A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data.
Which solution will meet these requirements?

A. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew recipe job.
B. Create a DataBrew dataset by using a JDBC driver to connect to the S3 bucket. Use a profile job.
C. Create a DataBrew dataset by using a JDBC driver to connect to the S3 bucket. Use a recipe job.
D. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a DataBrew profile job.

Antwort: A

Begründung:
AWS Glue DataBrew supports datasets sourced directly from Amazon S3 paths. To clean and normalize data, AWS documentation specifies using DataBrew recipes, which define transformation steps such as standardization, deduplication, and formatting.
A profile job is used only for data analysis and statistics generation, not for transformation. A recipe job applies actual transformations to the data and writes the output to Amazon S3.
JDBC connections are used for relational databases, not S3 buckets. Therefore, options C and D are invalid.
AWS clearly documents that the correct workflow is:
* Create a DataBrew dataset from an S3 path
* Define transformations using a recipe
* Run a recipe job to produce cleaned data
Thus, Option B is the correct and AWS-aligned answer.

233. Frage
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?

A. Check AWS CloudTrail event history for the creation of the resources.
B. Create code to evaluate each instance's memory and compute usage.
C. Run AWS Compute Optimizer.
D. Add cost allocation tags to the resources. Activate the tags in AWS Billing and Cost Management.

Antwort: C

234. Frage
An ML engineer is deploying a generative AI model-based customer support agent that uses Amazon SageMaker AI for inference. The customer support agent must respond to customer questions about topics such as shipping policies, refund processes, and account management. The generative AI model generates one token at a time.
Customers report dissatisfaction with how long the customer support agent takes to generate lengthy responses to questions. The ML engineer must apply an inference optimization technique to improve the performance of the customer support agent.
Which solution will meet this requirement?

A. Speculative decoding
B. Fast model loading
C. Compilation
D. Quantization

Antwort: A

Begründung:
Option B is correct because AWS documentation states that speculative decoding is specifically a technique to speed up the decoding process of large language models. The question highlights that the model generates one token at a time and that users are unhappy with the time needed to generate lengthy responses. That is exactly the stage of inference targeted by speculative decoding. AWS explains that this method improves latency without compromising the quality of the generated text.
According to AWS, speculative decoding works by using a smaller, faster draft model to generate candidate tokens first. The larger target model then verifies those tokens. AWS further explains that the draft model can generate multiple candidate tokens quickly, and the target model evaluates them in parallel, which speeds up the final response. This directly addresses the problem in the scenario: the customer support agent is slow not because of startup time, but because long answers require many decoding steps.
The other options are not the best fit. Compilation reduces deployment time and auto-scaling latency through ahead-of-time compilation, but AWS documents it primarily as helping model deployment and hardware optimization rather than token-by-token response generation. Quantization reduces hardware requirements and can lower cost, but the docs do not present it as the most direct answer to slow sequential decoding. Fast model loading improves how quickly the model loads onto instances, which helps startup and scale-out time, not long response generation after the model is already serving traffic. Therefore, the best AWS-documented answer is B.

235. Frage
A medical company is using AWS to build a tool to recommend treatments for patients. The company has obtained health records and self-reported textual information in English from patients. The company needs to use this information to gain insight about the patients.
Which solution will meet this requirement with the LEAST development effort?

A. Use Amazon Comprehend Medical to summarize the data.
B. Use Amazon Kendra to create a quick-search tool to query the data.
C. Use Amazon SageMaker to build a recurrent neural network (RNN) to summarize the data.
D. Use the Amazon SageMaker Sequence-to-Sequence (seq2seq) algorithm to create a text summary from the data.

Antwort: A

236. 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?

A. Use Managed Spot Training.
B. Use SageMaker Training Compiler.
C. Use SageMaker managed warm pools.
D. Use the SageMaker distributed data parallelism (SMDDP) library.

Antwort: C

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 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

237. Frage
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