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Question 1:
An e-commerce company is planning to integrate Amazon Bedrock for customer support and product recommendation. They want a solution that provides personalized product suggestions based on user interactions in real-time. Additionally, they need a secure environment where sensitive user data remains within a specific AWS region.
Which feature of Amazon Bedrock would best address their need for data security?
A. Knowledge bases
B. Serverless environment with automated scaling
C. AWS Identity and Access Management (IAM) integration
D. Data encryption in transit and at rest
Answer: D
Explanation:
Amazon Bedrock employs robust encryption techniques to protect data both during transmission (in transit) and when stored (at rest). This ensures that sensitive user data remains secure and confidential, meeting the company‘s data security requirements.
Incorrect Options:
A. Knowledge bases: Knowledge bases enhance the quality of model responses but don‘t directly address data security.
B. Serverless environment with automated scaling: This feature optimizes resource utilization and cost-efficiency but doesn‘t directly impact data security.
C. AWS IAM integration: IAM is a broader AWS service for managing access to resources. While it can be used to control access to Bedrock, it doesn‘t provide end-to-end encryption of data.
Question 2:
A data scientist is training a machine learning model using a large dataset with various features. During the initial evaluation, the model appears to be overfitting. Which combination of techniques can the data scientist apply to reduce overfitting and improve generalization? (Choose TWO)
A. Use cross-validation by rotating through training and validation sets.
B. Increase the number of parameters in the model to capture more data patterns.
C. Apply regularization to penalize extreme model parameters.
D. Reduce the amount of training data to limit the complexity of the model.
E. Avoid using dimensionality reduction techniques to retain all feature information.
Answer: A and C
Explanation:
A. Cross-validation: This technique helps assess the model‘s performance on unseen data. By splitting the data into multiple folds and training the model on different 1 subsets, cross-validation can provide a more reliable estimate of the model‘s generalization performance.
C. Regularization: Regularization techniques, such as L1 and L2 regularization, add a penalty term to the loss function. This penalty discourages the model from assigning large weights to features, reducing overfitting.
Incorrect Options:
B. Increase the number of parameters: Adding more parameters can increase the model‘s complexity and make it more prone to overfitting.
D. Reduce the amount of training data: More training data generally improves model performance. Reducing the data can lead to underfitting.
E. Avoid dimensionality reduction: Dimensionality reduction techniques can help reduce overfitting by removing irrelevant or redundant features.
Question 3:
A company wants to use AWS Config to maintain a detailed history of configuration changes across all of its resources for auditing purposes. They need to ensure the history of these changes is available long-term for regulatory requirements.
Which approach will allow them to retain and access this history over an extended period?
A. Enable configuration items and configure periodic evaluations.
B. Use configuration snapshots and store them in Amazon S3.
C. Enable AWS Config Rules and use CloudTrail to track changes.
D. Use a configuration stream and set up an Amazon RDS database for storage.
Answer: B
Explanation:
Configuration snapshots capture the complete configuration state of your resources at a specific point in time. By regularly scheduling configuration snapshots and storing them in S3, you can retain a detailed history of your resource configurations over an extended period. This allows you to easily audit changes, identify potential security risks, and comply with regulatory requirements.
Incorrect Options:
A. Enable configuration items and configure periodic evaluations: While this will provide some level of historical data, it might not be sufficient for long-term retention and detailed auditing.
C. Use AWS Config Rules and CloudTrail: These services are primarily used for monitoring and auditing, not for long-term storage of configuration history.
D. Use configuration stream and store in Amazon RDS: Configuration Stream provides a near-real-time feed of configuration changes. While you can store this data in RDS, it might not be the most efficient or cost-effective solution for long-term storage. S3 is a more suitable and cost-effective option for storing large amounts of historical data.
Question 4:
An organization needs to ensure that its AWS resources comply with regulatory standards. They decide to implement AWS Config to monitor and manage the configuration of these resources. The organization also requires automated alerts for non-compliant resources and periodic compliance checks.
Which combination of AWS Config features should they enable to meet these requirements? (Choose TWO)
A. Configuration Items
B. AWS Config Rules with periodic evaluation
C. Configuration Stream
D. Custom rules created with AWS Lambda
E. Real-time proactive evaluation mode
Answer: B and D
Explanation:
Correct Options:
B. AWS Config Rules with periodic evaluation: AWS Config Rules allow you to define specific compliance rules and evaluate your resources against them. By scheduling periodic evaluations, you can ensure that your resources remain compliant over time.
D. Custom rules created with AWS Lambda: Custom rules provide flexibility to create complex compliance checks that are not covered by built-in rules. You can use Lambda functions to define custom logic and trigger alerts for non-compliant resources.
Incorrect Options:
A. Configuration Items: Configuration Items are simply the configurations of your resources. They don‘t provide automated compliance checks or alerts.
C. Configuration Stream: Configuration Stream provides a near-real-time feed of configuration changes. While it can be used for auditing and troubleshooting, it doesn‘t directly provide automated compliance checks.
E. Real-time proactive evaluation mode: While this mode can provide faster detection of configuration changes, it‘s not necessary for periodic compliance checks. It might be overkill for many use cases and can increase costs.
Question 5:
A company is concerned about potential Distributed Denial of Service (DDoS) attacks on its application, which has been targeted in the past. They require advanced protection, including automatic mitigation for complex DDoS threats and access to AWSs DDoS Response Team (DRT) for real-time support during incidents.
Which AWS service should they use to meet these requirements?
A. AWS Shield Standard
B. AWS Shield Advanced
C. AWS WAF
D. Amazon CloudWatch
Answer: B
Explanation:
AWS Shield Advanced offers a comprehensive suite of DDoS protection features, including:
Automated response: It automatically detects and mitigates DDoS attacks, reducing the need for manual intervention.
Access to DRT: The DRT provides expert support and guidance during large-scale DDoS attacks.
Global network of mitigation systems: It leverages a global network of mitigation systems to protect against a wide range of DDoS attacks.
Incorrect Options:
A. AWS Shield Standard: While Shield Standard provides basic protection against common DDoS attacks, it doesn‘t offer the advanced features and support provided by Shield Advanced.
C. AWS WAF: WAF is a web application firewall that protects web applications from web attacks like SQL injection and cross-site scripting. It doesn‘t provide comprehensive DDoS protection.
D. Amazon CloudWatch: CloudWatch is a monitoring and logging service that can be used to track and analyze system metrics. It‘s not a security service and doesn‘t provide DDoS protection.
For a full set of 355 questions. Go to
https://skillcertpro.com/product/aws-machine-learning-engineer-associate-mla-c01-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
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Question 6:
A media company wants to generate synthetic data to improve the training of its recommendation model and is considering Amazon Bedrock for this purpose. The company plans to use a foundation model for content generation and requires secure, scalable data handling. Which Amazon Bedrock feature ensures data privacy and compliance during content generation?
A. Integration with Amazon CloudWatch for monitoring
B. On-demand pricing based on token usage
C. Region-specific data handling and encryption
D. Provisioned throughput mode for steady workloads
Answer: C
Explanation:
Amazon Bedrock ensures data privacy and compliance by handling data within specific regions. This means that your data remains within the chosen region, adhering to regional data residency and compliance requirements. Additionally, Bedrock employs robust encryption techniques to protect data both at rest and in transit.
Incorrect Options:
A. Integration with Amazon CloudWatch for monitoring: While CloudWatch is useful for monitoring, it doesn‘t directly address data privacy and compliance.
B. On-demand pricing based on token usage: This is a pricing model, not a security feature.
D. Provisioned throughput mode for steady workloads: This is a deployment option for handling predictable workloads, not a data privacy feature.
Question 7:
A financial institution wants to detect fraudulent activities in real time to protect against payment fraud and account takeovers. They plan to use Amazon Fraud Detector for this purpose and need to automatically assess each transaction based on its risk level.
Which step in Amazon Fraud Detectors workflow directly identifies transactions that are likely to be fraudulent?
A. Feature engineering to prepare data features
B. Real-time evaluation using fraud score
C. Model deployment to a production environment
D. Data ingestion of historical transaction data
Answer: B
Explanation:
Amazon Fraud Detector assigns a fraud score to each transaction in real-time based on the trained model‘s evaluation. A higher fraud score indicates a higher likelihood of fraudulent activity. This score can be used to trigger alerts, block transactions, or initiate further investigation.
Incorrect Options:
A. Feature engineering: This step prepares the data for model training but doesn‘t directly identify fraudulent transactions.
C. Model deployment: Deploying the model makes it available for prediction, but it doesn‘t directly identify fraudulent transactions.
D. Data ingestion: Ingesting historical data is a necessary step for training the model, but it doesn‘t involve real-time assessment of transactions.
Question 8:
An e-commerce company wants to integrate Amazon Bedrock to enhance its customer support system with a generative AI chatbot. The company expects usage to fluctuate significantly, with peak usage during holiday sales and lower usage during off-peak times. They also want to avoid managing infrastructure manually.
Which Amazon Bedrock feature will best support these requirements?
A. Foundation models with fine-tuning capabilities
B. Provisioned throughput mode for predictable traffic
C. Serverless environment with automatic scaling
D. Knowledge bases to enhance model responses
Answer: C
Explanation:
A serverless environment with automatic scaling is the ideal choice for the e-commerce company‘s use case. It allows the chatbot to handle fluctuating traffic without manual intervention. During peak usage periods, the infrastructure will automatically scale up to accommodate the increased load, and during off-peak times, it will scale down to minimize costs.
Incorrect Options:
A. Foundation models with fine-tuning capabilities: While fine-tuning is important for customizing the model to specific use cases, it doesn‘t directly address the scalability and cost-efficiency requirements.
B. Provisioned throughput mode: This mode is suitable for predictable workloads. For a use case with fluctuating traffic, a serverless environment is more flexible and cost-effective.
D. Knowledge bases: Knowledge bases can enhance the quality of chatbot responses, but they don‘t directly address the scalability and cost-efficiency requirements.
Question 9:
A machine learning engineer is using Amazon SageMaker Debugger to monitor a training job. The engineer notices that the models validation loss starts to increase while the training loss continues to decrease. What action should the engineer take to address this issue?
A. Increase the learning rate to improve the model‘s performance.
B. Use early stopping to prevent further overfitting.
C. Add more features to the model to increase its complexity.
D. Reduce the amount of training data to prevent overfitting.
Answer: B
Explanation:
When the validation loss starts to increase while the training loss continues to decrease, it indicates that the model is overfitting the training data. Early stopping is a technique that can be used to prevent overfitting by stopping the training process when the validation loss starts to increase.
Incorrect Options:
A. Increase the learning rate: Increasing the learning rate can accelerate the training process, but it can also lead to instability and overfitting.
C. Add more features: Adding more features can increase the model‘s complexity and make it more prone to overfitting.
D. Reduce the amount of training data: Reducing the amount of training data can lead to underfitting, as the model may not have enough data to learn the underlying patterns.
Question 10:
A data scientist is working to improve the generalization of their machine learning model and reduce overfitting. They have been advised to use cross-validation, add more training data, and apply regularization techniques. Which of the following best describes the purpose and impact of using regularization?
A. Regularization simplifies the model by penalizing extreme parameter values, reducing overfitting risk.
B. Regularization increases model complexity to improve performance on diverse datasets.
C. Regularization rotates through different subsets of data to enhance model generalization.
D. Regularization augments data size by adding synthetic samples from minority classes.
Answer: A
Explanation:
Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. This penalty term discourages the 1 model from assigning large weights to features, effectively simplifying the model and making it less prone to overfitting.
Incorrect Options:
B. Regularization increases model complexity: Regularization actually reduces model complexity by penalizing complex models.
C. Regularization rotates through different subsets of data: Cross-validation is the technique used for rotating through different subsets of data to assess model generalization.
D. Regularization augments data size by adding synthetic samples: Data augmentation is a technique used to increase the size of the training dataset, but it‘s not directly related to regularization.
For a full set of 355 questions. Go to
https://skillcertpro.com/product/aws-machine-learning-engineer-associate-mla-c01-exam-questions/
SkillCertPro offers detailed explanations to each question which helps to understand the concepts better.
It is recommended to score above 85% in SkillCertPro exams before attempting a real exam.
SkillCertPro updates exam questions every 2 weeks.
You will get life time access and life time free updates
SkillCertPro assures 100% pass guarantee in first attempt.