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AWS-Certified-Machine-Learning-Specialty Valid Exam Camp, AWS-Certified-Machine-Learning-Specialty Exam Dumps

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The AWS Certified Machine Learning - Specialty exam consists of 65 multiple-choice and multiple-response questions, and candidates have three hours to complete the exam. AWS-Certified-Machine-Learning-Specialty exam tests the candidate's ability to design, implement, deploy, and maintain machine learning solutions on the AWS platform. To be eligible for the exam, candidates need to have a minimum of one year of experience in developing and maintaining machine learning solutions on the AWS platform. Upon successful completion of the exam, candidates will receive the AWS Certified Machine Learning - Specialty certification, which is recognized globally and demonstrates their expertise in the field of machine learning on the AWS platform.

To prepare for the Amazon MLS-C01 exam, candidates should have experience with machine learning, data science, and AWS services. They should also have a strong understanding of statistics, linear algebra, and calculus. Candidates can prepare for the exam by taking training courses, reading AWS documentation, and practicing with sample questions and exams.

The Amazon AWS-Certified-Machine-Learning-Specialty Exam is designed to test an individual's knowledge and proficiency in a number of key areas, including data preparation, feature engineering, model selection, and optimization. Candidates are also expected to have a deep understanding of the AWS services and tools that are used for machine learning, including Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q243-Q248):

NEW QUESTION # 243
An agriculture company wants to improve crop yield forecasting for the upcoming season by using crop yields from the last three seasons. The company wants to compare the performance of its new scikit-learn model to the benchmark.
A data scientist needs to package the code into a container that computes both the new model forecast and the benchmark.
The data scientist wants AWS to be responsible for the operational maintenance of the container.
Which solution will meet these requirements?

  • A. Package the code into a custom-built container. Push the container to AWS Fargate.
  • B. Package the code by extending an Amazon SageMaker scikit-learn container.
  • C. Package the code as the training script for an Amazon SageMaker scikit-learn container.
  • D. Package the code into a custom-built container. Push the container to Amazon Elastic Container Registry (Amazon ECR).

Answer: B

Explanation:
To compare a custom scikit-learn model with a benchmark model in a managed environment, the most effective and maintainable solution is to extend an existing SageMaker scikit-learn container.
"If you are using a framework like scikit-learn and need custom logic, you can extend the prebuilt SageMaker containers to include your own inference or training scripts. AWS manages the container base and you only maintain your code." This approach allows the data scientist to maintain control over model logic while letting SageMaker handle the container lifecycle, scaling, and infrastructure management-which aligns with the requirement for AWS to handle operational maintenance.


NEW QUESTION # 244
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

  • A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account but they run outside of VPCs.
  • B. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  • C. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
  • D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.

Answer: A


NEW QUESTION # 245
A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.
Which set of actions should the ML specialist take to meet these requirements?

  • A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.
  • B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.
  • C. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.
  • D. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.

Answer: A

Explanation:
The correct solution for granting permissions for data preprocessing is to use the following steps:
Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. This role allows the ML specialist to run Processing jobs from the notebook code1 Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. This role allows the Processing job to access the data in the encrypted S3 bucket, decrypt it with the KMS CMK, and pull the container image from ECR23 The other options are incorrect because they either miss some permissions or use unnecessary steps. For example:
Option A uses a single IAM role for both the notebook instance and the Processing job. This role may have more permissions than necessary for the notebook instance, which violates the principle of least privilege4 Option C sets up both an S3 endpoint and a KMS endpoint in the default VPC. These endpoints are not required for the Processing job to access the data in the encrypted S3 bucket. They are only needed if the Processing job runs in network isolation mode, which is not specified in the question.
Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.
References:
1: Create an Amazon SageMaker Notebook Instance - Amazon SageMaker
2: Create a Processing Job - Amazon SageMaker
3: Use AWS KMS-Managed Encryption Keys - Amazon Simple Storage Service
4: IAM Best Practices - AWS Identity and Access Management
5: Network Isolation - Amazon SageMaker
6: Understanding and Getting Your Security Credentials - AWS General Reference


NEW QUESTION # 246
A company provisions Amazon SageMaker notebook instances for its data science team and creates Amazon VPC interface endpoints to ensure communication between the VPC and the notebook instances. All connections to the Amazon SageMaker API are contained entirely and securely using the AWS network. However, the data science team realizes that individuals outside the VPC can still connect to the notebook instances across the internet.
Which set of actions should the data science team take to fix the issue?

  • A. Modify the notebook instances' security group to allow traffic only from the CIDR ranges of the VPC. Apply this security group to all of the notebook instances' VPC interfaces.
  • B. Create an IAM policy that allows the sagemaker:CreatePresignedNotebooklnstanceUrl and sagemaker:DescribeNotebooklnstance actions from only the VPC endpoints. Apply this policy to all IAM users, groups, and roles used to access the notebook instances.
  • C. Change the network ACL of the subnet the notebook is hosted in to restrict access to anyone outside the VPC.
  • D. Add a NAT gateway to the VPC. Convert all of the subnets where the Amazon SageMaker notebook instances are hosted to private subnets. Stop and start all of the notebook instances to reassign only private IP addresses.

Answer: B


NEW QUESTION # 247
A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format.
During model evaluation, the data scientist discovered that the model recommends certain stopwords such as
"a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?

  • A. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.
  • B. Remove the stop words from the blog post data by using the Count Vectorizer function in the scikit- learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
  • C. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
  • D. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.

Answer: B

Explanation:
The data scientist should remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library, and replace the blog post data in the S3 bucket with the results of the vectorizer. This is because:
* The Count Vectorizer function is a tool that can convert a collection of text documents to a matrix of token counts 1. It also enables the pre-processing of text data prior to generating the vector representation, such as removing accents, converting to lowercase, and filtering out stop words 1. By using this function, the data scientist can remove the stop words such as "a," "an," and "the" from the blog post data, and obtain a numerical representation of the text that can be used as input for the NTM algorithm.
* The NTM algorithm is a neural network-based topic modeling technique that can learn latent topics from a corpus of documents 2. It can be used to recommend tags from blog posts by finding the most probable topics for each document, and ranking the words associated with each topic 3. However, the NTM algorithm does not perform any text pre-processing by itself, so it relies on the quality of the input data. Therefore, the data scientist should replace the blog post data in the S3 bucket with the results of the vectorizer, to ensure that the NTM algorithm does not include the stop words in the tag recommendations.
* The other options are not suitable for the following reasons:
* Option A is not relevant because the Amazon Comprehend entity recognition API operations are used to detect and extract named entities from text, such as people, places, organizations, dates, etc4. This is not the same as removing stop words, which are common words that do not carry much meaning or information. Moreover, removing the detected entities from the blog post data may reduce the quality and diversity of the tag recommendations, as some entities may be relevant and useful as tags.
* Option B is not optimal because the SageMaker built-in principal component analysis (PCA) algorithm is used to reduce the dimensionality of a dataset by finding the most important features that capture the maximum amount of variance in the data 5. This is not the same as removing stop words, which are words that have low variance and high frequency in the data. Moreover, replacing the blog post data in the S3 bucket with the results of the PCA algorithm may not be compatible with the input format expected by the NTM algorithm, which requires a bag-of-words representation of the text 2.
* Option C is not suitable because the SageMaker built-in Object Detection algorithm is used to detect and localize objects in images 6. This is not related to the task of recommending tags from blog posts, which are text documents. Moreover, using the Object Detection algorithm instead of the NTM algorithm would require a different type of input data (images instead of text), and a different type of output data (bounding boxes and labels instead of topics and words).
Neural Topic Model (NTM) Algorithm
Introduction to the Amazon SageMaker Neural Topic Model
Amazon Comprehend - Entity Recognition
sklearn.feature_extraction.text.CountVectorizer
Principal Component Analysis (PCA) Algorithm
Object Detection Algorithm


NEW QUESTION # 248
......

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