MLA-C01日本語学習内容をダウンロードし、AWS Certified Machine Learning Engineer - Associateをパスします

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Model Development30%- Algorithm selection and model architecture
- Hyperparameter optimization
- Training and validation strategies
- Distributed training
- ML frameworks (SageMaker, built-in algorithms)
- Transfer learning and fine-tuning
Data Processing22%- Data validation and quality assessment
- Data preprocessing and feature engineering
- Handling imbalanced data
- Data ingestion and transformation
- Data pipelining with AWS services ( Glue, Data Brew, etc.)
MLOps and Monitoring28%- Security and access management for ML
- Incident response and remediation
- Cost optimization for ML workloads
- CI/CD pipelines for ML
- Model lineage and reproducibility
- Model monitoring and drift detection
Model Deployment and Inference20%- Inference optimization (latency, throughput)
- Model versioning and rollback
- A/B testing and shadow mode deployment
- Model deployment strategies (real-time, batch)
- SageMaker endpoints configuration

>> MLA-C01日本語学習内容 <<

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Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q60-Q65):

質問 # 60
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

正解：D


質問 # 61
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?

正解：D

解説：
Accuracy measures the proportion of correctly predicted labels (both positive and negative) out of the total predictions. It is the appropriate metric when the goal is to maximize the correct predictions of both positive and negative labels. However, it assumes that the classes are balanced; if the classes are imbalanced, other metrics like precision, recall, or specificity may be more relevant depending on the specific needs.


質問 # 62
A company wants to improve its customer retention ML model. The current model has 85% accuracy and a new model shows 87% accuracy in testing. The company wants to validate the new model's performance in production.
Which solution will meet these requirements?

正解：D

解説：
AWS ML best practices recommend A/B testing to validate model improvements in production while minimizing risk. By routing a controlled portion of live traffic (for example, 20%) to the new model and keeping the majority of traffic on the existing model, the company can directly compare real-world performance using the same data distribution.
This approach allows statistically meaningful comparison of business metrics such as customer retention, rather than relying solely on offline accuracy. It also limits potential negative impact if the new model underperforms in production.
Deploying the new model to 100% of traffic (Option A) introduces unnecessary risk. Offline analysis (Option C) does not reflect live user behavior. Alternating deployments (Option D) introduces confounding factors such as time-based effects.
Therefore, A/B testing is the correct solution.


質問 # 63
A company has collected customer comments on its products, rating them as safe or unsafe, using decision trees. The training dataset has the following features: id, date, full review, full review summary, and a binary safe/unsafe tag. During training, any data sample with missing features was dropped. In a few instances, the test set was found to be missing the full review text field.
For this use case, which is the most effective course of action to address test data samples with missing features?

正解：C

解説：
In this case, a full review summary usually contains the most descriptive phrases of the entire review and is a valid stand-in for the missing full review text field.


質問 # 64
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

正解：D


質問 # 65
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