Jack Hill Jack Hill's صفحة الملف الشخصي

Jack Hill Jack Hill

0 دورة ملتحَق بها • 0 اكتملت الدورة

سيرة شخصية

Quiz Amazon - AWS-Certified-Machine-Learning-Specialty - Latest AWS Certified Machine Learning - Specialty Prepaway Dumps

2025 Latest TorrentExam AWS-Certified-Machine-Learning-Specialty PDF Dumps and AWS-Certified-Machine-Learning-Specialty Exam Engine Free Share: https://drive.google.com/open?id=1s1-EFzUcctb5_HnJcWvTD9GqjTLoZN-S

For some candidates who are caring about the protection of the privacy, our AWS-Certified-Machine-Learning-Specialty exam materials will be your best choice. We respect the personal information of our customers. If you buy AWS-Certified-Machine-Learning-Specialty exam materials from us, we can ensure you that your personal information, such as the name and email address will be protected well. Once the order finishes, your personal information will be concealed. In addition, we are pass guarantee and money back guarantee. If you fail to pass the exam after buying AWS-Certified-Machine-Learning-Specialty Exam Dumps from us, we will refund your money.

The AWS Certified Machine Learning - Specialty Exam is a rigorous, four-hour exam that consists of multiple-choice and multiple-response questions. AWS-Certified-Machine-Learning-Specialty Exam covers a range of topics, including data engineering, exploratory data analysis, modeling, and machine learning implementation and operations. To pass the exam, candidates must achieve a minimum score of 750 out of 1000.

>> AWS-Certified-Machine-Learning-Specialty Prepaway Dumps <<

AWS-Certified-Machine-Learning-Specialty Download Pdf & New AWS-Certified-Machine-Learning-Specialty Braindumps Files

Attending TorrentExam, you will have best exam dumps for the certification of AWS-Certified-Machine-Learning-Specialty exam tests. We offer you the most accurate AWS-Certified-Machine-Learning-Specialty exam answers that will be your key to pass the certification exam in your first try. There are the best preparation materials for your AWS-Certified-Machine-Learning-Specialty Practice Test in our website to guarantee your success in a short time. Please totally trust the accuracy of questions and answers.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q310-Q315):

NEW QUESTION # 310
A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker.
Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

A. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster.
B. Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.
C. Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3.
D. Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3.

Answer: D

NEW QUESTION # 311
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

A. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
B. Store datasets as global tables in Amazon DynamoDB.
C. Store datasets as files in Amazon S3.
D. Store datasets as tables in a multi-node Amazon Redshift cluster.

Answer: D

NEW QUESTION # 312
An obtain relator collects the following data on customer orders: demographics, behaviors, location, shipment progress, and delivery time. A data scientist joins all the collected datasets. The result is a single dataset that includes 980 variables.
The data scientist must develop a machine learning (ML) model to identify groups of customers who are likely to respond to a marketing campaign.
Which combination of algorithms should the data scientist use to meet this requirement? (Select TWO.)

A. Se mantic feg mentation
B. Factorization machines (FM)
C. Principal component analysis (PCA)
D. K-means
E. Latent Dirichlet Allocation (LDA)

Answer: C,D

Explanation:
The data scientist should use K-means and principal component analysis (PCA) to meet this requirement. K-means is a clustering algorithm that can group customers based on their similarity in the feature space. PCA is a dimensionality reduction technique that can transform the original 980 variables into a smaller set of uncorrelated variables that capture most of the variance in the data. This can help reduce the computational cost and noise in the data, and improve the performance of the clustering algorithm.
References:
Clustering - Amazon SageMaker
Dimensionality Reduction - Amazon SageMaker

NEW QUESTION # 313
A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

A. Concatenate the features with high correlation scores by using a Jupyter notebook.
B. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
C. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
D. Drop the features with low correlation scores by using a Jupyter notebook.

Answer: C

Explanation:
The best feature engineering strategy for this scenario is to apply dimensionality reduction by using the principal component analysis (PCA) algorithm. PCA is a technique that transforms a large set of correlated features into a smaller set of uncorrelated features called principal components. This can help reduce the complexity and noise in the data, improve the performance and interpretability of the model, and avoid overfitting. Amazon SageMaker provides a built-in PCA algorithm that can be used to perform dimensionality reduction on tabular data. The ML specialist can use Amazon SageMaker to train and deploy the PCA model, and then use the output of the PCA model as the input for the classification model.
References:
Dimensionality Reduction with Amazon SageMaker
Amazon SageMaker PCA Algorithm

NEW QUESTION # 314
A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2.000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.
What changes in model training would MOST likely improve the model's F1 score? (Select TWO.)

A. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
B. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit-iearn multi-dimensional scaling (MDS) algorithm.
C. Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.
D. Use the SageMaker k-means algorithm with k of less than 1.000 to train the model
E. Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than
1,000 to train the model.

Answer: A,E

Explanation:
Explanation
Option A is correct because reducing the number of features with the SageMaker PCA algorithm can help remove noise and redundancy from the data, and improve the model's performance. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of linearly uncorrelated features called principal components. The SageMaker linear learner algorithm supports PCA as a built-in feature transformation option.
Option E is correct because using the SageMaker k-NN algorithm with a dimension reduction target of less than 1,000 can help the model learn from the similarity of the data points, and improve the model's performance. k-NN is a non-parametric algorithm that classifies an input based on the majority vote of its k nearest neighbors in the feature space. The SageMaker k-NN algorithm supports dimension reduction as a built-in feature transformation option.
Option B is incorrect because using the scikit-learn MDS algorithm to reduce the number of features is not a feasible option, as MDS is a computationally expensive technique that does not scale well to large datasets. MDS is a dimensionality reduction technique that tries to preserve the pairwise distances between the original data points in a lower-dimensional space.
Option C is incorrect because setting the predictor type to regressor would change the model's objective from classification to regression, which is not suitable for the given problem. A regressor model would output a continuous value instead of a binary label for each phone.
Option D is incorrect because using the SageMaker k-means algorithm with k of less than 1,000 would not help the model classify the phones, as k-means is a clustering algorithm that groups the data points into k clusters based on their similarity, without using any labels. A clustering model would not output a binary label for each phone.
References:
Amazon SageMaker Linear Learner Algorithm
Amazon SageMaker K-Nearest Neighbors (k-NN) Algorithm
[Principal Component Analysis - Scikit-learn]
[Multidimensional Scaling - Scikit-learn]

NEW QUESTION # 315
......

If you want to pass the exam smoothly buying our AWS-Certified-Machine-Learning-Specialty study materials is your ideal choice. They can help you learn efficiently, save your time and energy and let you master the useful information. Our passing rate of AWS-Certified-Machine-Learning-Specialty study materials is very high and you needn’t worry that you have spent money and energy on them but you gain nothing. We provide the great service after you purchase our AWS-Certified-Machine-Learning-Specialty Study Materials and you can contact our customer service at any time during one day.

AWS-Certified-Machine-Learning-Specialty Download Pdf: https://www.torrentexam.com/AWS-Certified-Machine-Learning-Specialty-exam-latest-torrent.html

What's more, part of that TorrentExam AWS-Certified-Machine-Learning-Specialty dumps now are free: https://drive.google.com/open?id=1s1-EFzUcctb5_HnJcWvTD9GqjTLoZN-S