AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM DUMPS COLLECTION | VALID EXAM AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY VCE: AWS CERTIFIED MACHINE LEARNING - SPECIALTY

AWS-Certified-Machine-Learning-Specialty Exam Dumps Collection | Valid Exam AWS-Certified-Machine-Learning-Specialty Vce: AWS Certified Machine Learning - Specialty

AWS-Certified-Machine-Learning-Specialty Exam Dumps Collection | Valid Exam AWS-Certified-Machine-Learning-Specialty Vce: AWS Certified Machine Learning - Specialty

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q271-Q276):

NEW QUESTION # 271
An Amazon SageMaker notebook instance is launched into Amazon VPC The SageMaker notebook references data contained in an Amazon S3 bucket in another account The bucket is encrypted using SSE-KMS The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)

  • A. A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
  • B. An S3 bucket owner that matches the notebook owner
  • C. An AWS KMS key policy that allows access to the customer master key (CMK)
  • D. A SageMaker notebook security group that allows access to Amazon S3
  • E. An 1AM role that allows access to the specific S3 bucket
  • F. A permissive S3 bucket policy

Answer: C,D,E

Explanation:
To access an Amazon S3 bucket in another account that is encrypted using SSE-KMS, the following are required:
A) An AWS KMS key policy that allows access to the customer master key (CMK). The CMK is the encryption key that is used to encrypt and decrypt the data in the S3 bucket. The KMS key policy defines who can use and manage the CMK. To allow access to the CMK from another account, the key policy must include a statement that grants the necessary permissions (such as kms:Decrypt) to the principal from the other account (such as the SageMaker notebook IAM role).
B) A SageMaker notebook security group that allows access to Amazon S3. A security group is a virtual firewall that controls the inbound and outbound traffic for the SageMaker notebook instance. To allow the notebook instance to access the S3 bucket, the security group must have a rule that allows outbound traffic to the S3 endpoint on port 443 (HTTPS).
C) An IAM role that allows access to the specific S3 bucket. An IAM role is an identity that can be assumed by the SageMaker notebook instance to access AWS resources. The IAM role must have a policy that grants the necessary permissions (such as s3:GetObject) to access the specific S3 bucket. The policy must also include a condition that allows access to the CMK in the other account.
The following are not required or correct:
D) A permissive S3 bucket policy. A bucket policy is a resource-based policy that defines who can access the S3 bucket and what actions they can perform. A permissive bucket policy is not required and not recommended, as it can expose the bucket to unauthorized access. A bucket policy should follow the principle of least privilege and grant the minimum permissions necessary to the specific principals that need access.
E) An S3 bucket owner that matches the notebook owner. The S3 bucket owner and the notebook owner do not need to match, as long as the bucket owner grants cross-account access to the notebook owner through the KMS key policy and the bucket policy (if applicable).
F) A SegaMaker notebook subnet ACL that allow traffic to Amazon S3. A subnet ACL is a network access control list that acts as an optional layer of security for the SageMaker notebook instance's subnet. A subnet ACL is not required to access the S3 bucket, as the security group is sufficient to control the traffic. However, if a subnet ACL is used, it must not block the traffic to the S3 endpoint.


NEW QUESTION # 272
An ecommerce company has observed that customers who use the company's website rarely view items that the website recommends to customers. The company wants to recommend items to customers that customers are more likely to want to purchase.
Which solution will meet this requirement in the SHORTEST amount of time?

  • A. Host the company's website on Amazon EC2 Accelerated Computing instances to increase the website response speed.
  • B. Host the company's website on Amazon EC2 GPU-based instances to increase the speed of the website's search tool.
  • C. Use Amazon SageMaker to train a Neural Collaborative Filtering (NCF) model to make product recommendations.
  • D. Integrate Amazon Personalize into the company's website to provide customers with personalized recommendations.

Answer: D

Explanation:
Amazon Personalize is a managed AWS service specifically designed to deliver personalized recommendations with minimal development time. It uses machine learning algorithms tailored for recommendation systems, making it highly suitable for applications where quick integration is essential. By using Amazon Personalize, the company can leverage existing customer data to generate real-time, personalized product recommendations that align better with customer preferences, enhancing the likelihood of customer engagement with recommended items.
Options involving EC2 instances with GPU or accelerated computing primarily enhance computational performance but do not inherently improve recommendation relevance, while Amazon SageMaker would require more development effort to achieve similar results.


NEW QUESTION # 273
Machine Learning Specialist is building a model to predict future employment rates based on a wide range of economic factors. While exploring the data, the Specialist notices that the magnitude of the input features vary greatly. The Specialist does not want variables with a larger magnitude to dominate the model.
What should the Specialist do to prepare the data for model training?

  • A. Apply the orthogonal sparse bigram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
  • B. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution.
  • C. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude.
  • D. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude.

Answer: C

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html


NEW QUESTION # 274
A retail company collects customer comments about its products from social media, the company website, and customer call logs. A team of data scientists and engineers wants to find common topics and determine which products the customers are referring to in their comments. The team is using natural language processing (NLP) to build a model to help with this classification.
Each product can be classified into multiple categories that the company defines. These categories are related but are not mutually exclusive. For example, if there is mention of "Sample Yogurt" in the document of customer comments, then "Sample Yogurt" should be classified as "yogurt," "snack," and "dairy product." The team is using Amazon Comprehend to train the model and must complete the project as soon as possible.
Which functionality of Amazon Comprehend should the team use to meet these requirements?

  • A. Custom classification with multi-class mode
  • B. Custom classification with multi-label mode
  • C. Built-in models
  • D. Custom entity recognition

Answer: B

Explanation:
The problem involves assigning multiple related, non-mutually exclusive categories (like "yogurt", "snack", and "dairy product") to customer comments referencing specific products. Amazon Comprehend provides custom classification capabilities, and in this case, the appropriate mode is multi-label, not multi-class.
"Use multi-label mode when each document can belong to more than one class. This is appropriate for situations where the categories are not mutually exclusive." This allows a single document to be tagged with several relevant labels simultaneously, which is precisely the use case described.


NEW QUESTION # 275
A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.
Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category.
The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise.
Which solution meets these requirements?

  • A. Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • B. Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • C. Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • D. Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Answer: B

Explanation:
Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and language. Amazon Comprehend also provides custom classification and custom entity recognition features that allow users to train their own models using their own data and labels. For the scenario of routing customer claims to different queues based on categories, Amazon Comprehend custom classification is a suitable solution. The custom classifier can be trained using a .csv file that contains the claim text and the claim label as columns. The custom classifier can then be used to process incoming claims and predict the labels using the Amazon Comprehend API. The predicted labels can be used to route the claims to the appropriate queue. This solution does not require any machine learning expertise or model deployment, and it can be easily integrated with the existing application.
The other options are not suitable because:
* Option A: Amazon SageMaker Object2Vec is an algorithm that can learn embeddings of objects such as words, sentences, or documents. It can be used for tasks such as text classification, sentiment analysis, or recommendation systems. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company.
* Option B: Amazon SageMaker Latent Dirichlet Allocation (LDA) is an algorithm that can discover the topics or themes in a collection of documents. It can be used for tasks such as topic modeling, document clustering, or text summarization. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company. Moreover, LDA does not provide labels for the topics, but rather a distribution of words for each topic, which may not match the existing categories of the claims.
* Option C: Amazon Textract is a service that can extract text and data from scanned documents or images. It can be used for tasks such as document analysis, data extraction, or form processing.
However, using this service is unnecessary and inefficient for the scenario, since the company already has the claim text and label in a database. Moreover, Amazon Textract does not provide custom classification features, so it cannot be used to train a custom classifier using the existing data and labels.
References:
* Amazon Comprehend Custom Classification
* Amazon SageMaker Object2Vec
* Amazon SageMaker Latent Dirichlet Allocation
* Amazon Textract


NEW QUESTION # 276
......

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