VALID AMAZON AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY REAL TEST, EXAM AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY REVIEW

Valid Amazon AWS-Certified-Machine-Learning-Specialty Real Test, Exam AWS-Certified-Machine-Learning-Specialty Review

Valid Amazon AWS-Certified-Machine-Learning-Specialty Real Test, Exam AWS-Certified-Machine-Learning-Specialty Review

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Understanding functional and technical aspects of AWS Certified Machine Learning Specialty Exam Exploratory Data Analysis

The following will be dicussed here:

  • Analyze and visualize data for machine learning
  • Sanitize and prepare data for modeling
  • Perform feature engineering

The AWS Certified Machine Learning - Specialty certification is ideal for individuals who want to enhance their career in the field of machine learning and data science. It is also a valuable credential for IT professionals who are looking for ways to improve their skills and stay up-to-date with the latest trends in cloud-based machine learning solutions. By earning the AWS Certified Machine Learning - Specialty Certification, individuals can demonstrate their expertise and credibility in this rapidly growing field, making them more attractive to potential employers and clients.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q100-Q105):

NEW QUESTION # 100
An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.
Which one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic Which solution will meet these requirements?

  • A. Canary release
  • B. A/B testing
  • C. Shadow deployment
  • D. Blue/green deployment

Answer: C

Explanation:
Explanation
The best solution for this scenario is to use shadow deployment, which is a technique that allows the company to run the new experimental model in parallel with the existing model, without exposing it to the end users. In shadow deployment, the company can route the same user requests to both models, but only return the responses from the existing model to the users. The responses from the new experimental model are logged and analyzed for quality and performance metrics, such as accuracy, latency, and resource consumption12.
This way, the company can validate the new experimental model in a production environment, without affecting the current live traffic or user experience.
The other solutions are not suitable, because they have the following drawbacks:
A: A/B testing is a technique that involves splitting the user traffic between two or more models, and comparing their outcomes based on predefined metrics. However, this technique exposes the new experimental model to a portion of the end users, which might affect their experience if the model is not reliable or consistent with the existing model3.
B: Canary release is a technique that involves gradually rolling out the new experimental model to a small subset of users, and monitoring its performance and feedback. However, this technique also exposes the new experimental model to some end users, and requires careful selection and segmentation of the user groups4.
D: Blue/green deployment is a technique that involves switching the user traffic from the existing model (blue) to the new experimental model (green) at once, after testing and verifying the new model in a separate environment. However, this technique does not allow the company to validate the new experimental model in a production environment, and might cause service disruption or inconsistency if the new model is not compatible or stable5.
References:
1: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog
2: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog
3: A/B Testing for Machine Learning Models | AWS Machine Learning Blog
4: Canary Releases for Machine Learning Models | AWS Machine Learning Blog
5: Blue-Green Deployments for Machine Learning Models | AWS Machine Learning Blog


NEW QUESTION # 101
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?

  • A. The learning rate should be increased because the optimization process was trapped at a local minimum.
  • B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
  • C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
  • D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.

Answer: D

Explanation:
https://kharshit.github.io/blog/2018/05/04/dropout-prevent-overfitting


NEW QUESTION # 102
An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models.
During the model evaluation, the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images.
Which of the following should be used to resolve this issue? (Choose two.)

  • A. Make the neural network architecture complex.
  • B. Add L2 regularization to the model.
  • C. Use gradient checking in the model.
  • D. Add vanishing gradient to the model.
  • E. Perform data augmentation on the training data.

Answer: C,E


NEW QUESTION # 103
A law firm handles thousands of contracts every day. Every contract must be signed. Currently, a lawyer manually checks all contracts for signatures.
The law firm is developing a machine learning (ML) solution to automate signature detection for each contract. The ML solution must also provide a confidence score for each contract page.
Which Amazon Textract API action can the law firm use to generate a confidence score for each page of each contract?

  • A. Use the Prediction API call on the documents. Return the signatures and confidence scores for each page.
  • B. Use the AnalyzeDocument API action. Set the FeatureTypes parameter to SIGNATURES. Return the confidence scores for each page.
  • C. Use the GetDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page
  • D. Use the StartDocumentAnalysis API action to detect the signatures. Return the confidence scores for each page.

Answer: B

Explanation:
The AnalyzeDocument API action is the best option to generate a confidence score for each page of each contract. This API action analyzes an input document for relationships between detected items. The input document can be an image file in JPEG or PNG format, or a PDF file. The output is a JSON structure that contains the extracted data from the document. The FeatureTypes parameter specifies the types of analysis to perform on the document. The available feature types are TABLES, FORMS, and SIGNATURES. By setting the FeatureTypes parameter to SIGNATURES, the API action will detect and extract information about signatures from the document. The output will include a list of SignatureDetection objects, each containing information about a detected signature, such as its location and confidence score. The confidence score is a value between 0 and 100 that indicates the probability that the detected signature is correct. The output will also include a list of Block objects, each representing a document page. Each Block object will have a Page attribute that contains the page number and a Confidence attribute that contains the confidence score for the page. The confidence score for the page is the average of the confidence scores of the blocks that are detected on the page. The law firm can use the AnalyzeDocument API action to generate a confidence score for each page of each contract by using the SIGNATURES feature type and returning the confidence scores from the SignatureDetection and Block objects.
The other options are not suitable for generating a confidence score for each page of each contract. The Prediction API call is not an Amazon Textract API action, but a generic term for making inference requests to a machine learning model. The StartDocumentAnalysis API action is used to start an asynchronous job to analyze a document. The output is a job identifier (JobId) that is used to get the results of the analysis with the GetDocumentAnalysis API action. The GetDocumentAnalysis API action is used to get the results of a document analysis started by the StartDocumentAnalysis API action. The output is a JSON structure that contains the extracted data from the document. However, both the StartDocumentAnalysis and the GetDocumentAnalysis API actions do not support the SIGNATURES feature type, and therefore cannot detect signatures or provide confidence scores for them.
References:
*AnalyzeDocument
*SignatureDetection
*Block
*Amazon Textract launches the ability to detect signatures on any document


NEW QUESTION # 104
A company is running a machine learning prediction service that generates 100 TB of predictions every day. A Machine Learning Specialist must generate a visualization of the daily precision- recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?

  • A. Run daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3.
    Give the Business team read-only access to S3.
  • B. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
  • C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team.
  • D. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.

Answer: C


NEW QUESTION # 105
......

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