AWS-Certified-Machine-Learning-Specialty Testdump - AWS-Certified-Machine-Learning-Specialty Questions Exam

We are specializing in the AWS-Certified-Machine-Learning-Specialty exam material especially focus on the service after sales as a leader in this field. In order to provide the top service on our AWS-Certified-Machine-Learning-Specialty study engine, our customer agents will work in 24/7. So after purchase, if you have any doubts about the AWS-Certified-Machine-Learning-Specialty learning guideyou can contact us. We Promise we will very happy to answer your question with more patience and enthusiasm and try our utmost to help you on the AWS-Certified-Machine-Learning-Specialty training questions.

Our AWS-Certified-Machine-Learning-Specialty real exam helps you not only to avoid all the troubles of learning but also to provide you with higher learning quality than other students'. At the same time, our AWS-Certified-Machine-Learning-Specialty exam materials have been kind enough to prepare the App version for you, so that you can download our AWS-Certified-Machine-Learning-Specialty practice prep to any electronic device, and then you can take all the learning materials with you and review no matter where you are.

>> AWS-Certified-Machine-Learning-Specialty Testdump <<

100% Pass Quiz High Hit-Rate Amazon - AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty Testdump

Pass4Test is the leading position in this field and famous for high pass rate. If you are headache about your qualification exams, our AWS-Certified-Machine-Learning-Specialty learning guide materials will be a great savior for you. Now it is your opportunity that we provide the best valid and professional AWS-Certified-Machine-Learning-Specialty study guide materials which have 100% pass rate. If you really want to Clear AWS-Certified-Machine-Learning-Specialty Exam and gain success one time, choosing us will be the wise thing for you. If you hesitate about us please pay attention on below about our satisfying service and high-quality AWS-Certified-Machine-Learning-Specialty guide torrent.

Amazon MLS-C01 certification is designed to validate an individual's expertise in machine learning on AWS platform. AWS Certified Machine Learning - Specialty certification is highly valued in the industry and is recognized globally. AWS-Certified-Machine-Learning-Specialty Exam covers a wide range of topics, including data preparation, feature engineering, model training, evaluation and deployment, and machine learning implementation and operations. AWS Certified Machine Learning - Specialty certification is ideal for individuals looking to build a career in machine learning on AWS and is highly valued by organizations that are looking to hire ML professionals.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q80-Q85):

NEW QUESTION # 80
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?

A. Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
B. Use the event tracker in Amazon Personalize to include real-time user interactions.
C. Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
D. Add event type and event value fields to the interactions dataset in Amazon Personalize.

Answer: D

NEW QUESTION # 81
A data scientist uses Amazon SageMaker Data Wrangler to obtain a feature summary from a dataset that the data scientist imported from Amazon S3. The data scientist notices that the prediction power for a dataset feature has a score of 1.
What is the cause of the score?

A. The data scientist did not process the features enough to accurately calculate prediction power.
B. The SageMaker Data Wrangler algorithm that the data scientist used did not find an optimal model fit for each feature to calculate the prediction power.
C. The data scientist did not fine-tune the training and validation split.
D. Target leakage occurred in the imported dataset.

Answer: D

Explanation:
A prediction power score of 1 indicates that the feature perfectly predicts the target variable, which usually suggests target leakage. Target leakage occurs when the feature includes information that would not be available at prediction time, leading to unrealistic performance.
From AWS documentation:
"Prediction power scores close to 1 might suggest target leakage in your dataset, as the feature may contain information about the target that wouldn't be available at inference."
- AWS SageMaker Data Wrangler documentation

NEW QUESTION # 82
A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?

A. Replace the current endpoint with a multi-model endpoint using SageMaker.
B. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
C. Increase the cooldown period for the scale-out activity.
D. Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.

Answer: C

Explanation:
The correct solution for changing the scaling behavior of the SageMaker instances is to increase the cooldown period for the scale-out activity. The cooldown period is the amount of time, in seconds, after a scaling activity completes before another scaling activity can start. By increasing the cooldown period for the scale- out activity, the ML team can ensure that the new instances are ready before launching additional instances. This will prevent over-scaling and reduce costs1 The other options are incorrect because they either do not solve the issue or require unnecessary steps. For example:
Option A decreases the cooldown period for the scale-in activity and increases the configured maximum capacity of instances. This option does not address the issue of launching additional instances before the new instances are ready. It may also cause under-scaling and performance degradation.
Option B replaces the current endpoint with a multi-model endpoint using SageMaker. A multi-model endpoint is an endpoint that can host multiple models using a single endpoint. It does not affect the scaling behavior of the SageMaker instances. It also requires creating a new endpoint and updating the application code to use it2 Option C sets up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
Amazon API Gateway is a service that allows users to create, publish, maintain, monitor, and secure APIs.
AWS Lambda is a service that lets users run code without provisioning or managing servers. These services do not affect the scaling behavior of the SageMaker instances. They also require creating and configuring additional resources and services34
1: Automatic Scaling - Amazon SageMaker
2: Create a Multi-Model Endpoint - Amazon SageMaker
3: Amazon API Gateway - Amazon Web Services
4: AWS Lambda - Amazon Web Services

NEW QUESTION # 83
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. 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.
B. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
C. 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.
D. 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.

Answer: D

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).
References:
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 # 84
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

A. Normalization transformation
B. One-hot encoding
C. Binarization
D. Tokenization

Answer: B

NEW QUESTION # 85
......

You will receive AWS-Certified-Machine-Learning-Specialty exam materials immediately after your payment is successful, and then, you can use AWS-Certified-Machine-Learning-Specialty test guide to learn. Everyone knows that time is very important and hopes to learn efficiently, especially for those who have taken a lot of detours and wasted a lot of time. Once they discover AWS-Certified-Machine-Learning-Specialty study braindumps, they will definitely want to seize the time to learn. At the same time, regardless of which mode you use, AWS-Certified-Machine-Learning-Specialty Test Guide will never limit your download times and the number of concurrent users. For the same information, you can use it as many times as you want, and even use together with your friends.

AWS-Certified-Machine-Learning-Specialty Questions Exam: https://www.pass4test.com/AWS-Certified-Machine-Learning-Specialty.html