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AWS-Certified-Machine-Learning-Specialty Exam Simulator Fee, AWS-Certified-Machine-Learning-Specialty Valid Test Tutorial

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Amazon AWS-Certified-Machine-Learning-Specialty is a certification exam that validates the skills and knowledge of professionals in machine learning on the Amazon Web Services (AWS) platform. AWS-Certified-Machine-Learning-Specialty exam is designed for individuals who want to demonstrate their ability to design, implement, deploy, and maintain machine learning solutions on AWS. By passing AWS-Certified-Machine-Learning-Specialty exam, professionals can showcase their expertise in machine learning, which is a highly in-demand skill in the tech industry.

Amazon MLS-C01 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 Exam also covers AWS services such as Amazon SageMaker, AWS Deep Learning AMIs, Amazon S3, Amazon EMR, and Amazon Redshift. AWS-Certified-Machine-Learning-Specialty exam is designed to assess the candidate's ability to use these services to build, train, and deploy ML models.

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The Amazon AWS-Certified-Machine-Learning-Specialty Exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, model selection and evaluation, and deployment. It also focuses on AWS-specific concepts such as SageMaker, AWS Deep Learning AMIs, and AWS Glue. The AWS Certified Machine Learning - Specialty certification exam is intended for individuals with at least one year of experience in building and deploying machine learning models on AWS.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q40-Q45):

NEW QUESTION # 40
A machine learning specialist is developing a regression model to predict rental rates from rental listings. A variable named Wall_Color represents the most prominent exterior wall color of the property. The following is the sample data, excluding all other variables:

The specialist chose a model that needs numerical input data.
Which feature engineering approaches should the specialist use to allow the regression model to learn from the Wall_Color data? (Choose two.)

A. Create three columns to encode the color in RGB format.
B. Apply integer transformation and set Red = 1, White = 5, and Green = 10.
C. Replace the color name string by its length.
D. Replace each color name by its training set frequency.
E. Add new columns that store one-hot representation of colors.

Answer: A,B

NEW QUESTION # 41
A 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 Diagram (OSB) transformation to apply a fixed-size sliding window to generate new features of a similar magnitude.
B. Apply normalization to ensure each field will have a mean of 0 and a variance of 1 to remove any significant magnitude
C. Apply quantile binning to group the data into categorical bins to keep any relationships in the data by replacing the magnitude with distribution
D. Apply the Cartesian product transformation to create new combinations of fields that are independent of the magnitude

Answer: B

Explanation:
Explanation
Normalization is a data preprocessing technique that can be used to scale the input features to a common range, such as [-1, 1] or [0, 1]. Normalization can help reduce the effect of outliers, improve the convergence of gradient-based algorithms, and prevent variables with a larger magnitude from dominating the model. One common method of normalization is standardization, which transforms each feature to have a mean of 0 and a variance of 1. This can be done by subtracting the mean and dividing by the standard deviation of each feature.
Standardization can be useful for models that assume the input features are normally distributed, such as linear regression, logistic regression, and support vector machines. References:
Data normalization and standardization: A video that explains the concept and benefits of data normalization and standardization.
Standardize or Normalize?: A blog post that compares different methods of scaling the input features.

NEW QUESTION # 42
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?

A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

Answer: A

Explanation:
Explanation
To build a robust serverless data lake on Amazon S3 that meets the requirements, the financial services company should use the following AWS services:
AWS Glue crawler: This is a service that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for the data, and then creates metadata tables in the AWS Glue Data Catalog1. The company can use an AWS Glue crawler to crawl the S3 data and infer the schema, format, and partition structure of the data. The crawler can also detect schema changes and update the metadata tables accordingly. This enables the company to support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum, which are serverless interactive query services that use the AWS Glue Data Catalog as a central location for storing and retrieving table metadata23.
AWS Lambda function: This is a service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume - there is no charge when your code is not running. You can also use AWS Lambda to create event-driven ETL pipelines, by triggering other AWS services based on events such as object creation or deletion in S3 buckets4. The company can use an AWS Lambda function to trigger an AWS Glue ETL job, which is a serverless way to extract, transform, and load data for analytics. The AWS Glue ETL job can perform various data processing tasks, such as converting data formats, filtering, aggregating, joining, and more.
AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos, and use that metadata to query and transform the data. The company can use the AWS Glue Data Catalog to search and discover metadata, such as table definitions, schemas, and partitions. The AWS Glue Data Catalog also integrates with Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, and AWS Glue ETL jobs, providing a consistent view of the data across different query and analysis services.
References:
1: What Is a Crawler? - AWS Glue
2: What Is Amazon Athena? - Amazon Athena
3: Amazon Redshift Spectrum - Amazon Redshift
4: What is AWS Lambda? - AWS Lambda
5: AWS Glue ETL Jobs - AWS Glue
6: What Is the AWS Glue Data Catalog? - AWS Glue

NEW QUESTION # 43
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. Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
B. Drop the features with low correlation scores by using a Jupyter notebook.
C. Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
D. Concatenate the features with high correlation scores by using a Jupyter notebook.

Answer: A

NEW QUESTION # 44
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

A. Serialize the trained model so the format is compressed for deployment. Build the image and upload it toDocker Hub.
B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with theregistry hostname and upload it to Amazon S3.
C. Build the Docker image with the inference code. Tag the Docker image with the registry hostname andupload it to Amazon ECR.
D. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

Answer: C

Explanation:
To deploy a model that was trained locally to Amazon SageMaker, the steps are:
* Build the Docker image with the inference code. The inference code should include the model loading, data preprocessing, prediction, and postprocessing logic. The Docker image should also include the dependencies and libraries required by the inference code and the model.
* Tag the Docker image with the registry hostname and upload it to Amazon ECR. Amazon ECR is a fully managed container registry that makes it easy to store, manage, and deploy container images. The registry hostname is the Amazon ECR registry URI for your account and Region. You can use the AWS CLI or the Amazon ECR console to tag and push the Docker image to Amazon ECR.
* Create a SageMaker model entity that points to the Docker image in Amazon ECR and the model artifacts in Amazon S3. The model entity is a logical representation of the model that contains the information needed to deploy the model for inference. The model artifacts are the files generated by the model training process, such as the model parameters and weights. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the model entity.
* Create an endpoint configuration that specifies the instance type and number of instances to use for hosting the model. The endpoint configuration also defines the production variants, which are the different versions of the model that you want to deploy. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint configuration.
* Create an endpoint that uses the endpoint configuration to deploy the model. The endpoint is a web service that exposes an HTTP API for inference requests. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Deploy a Model on Amazon SageMaker
AWS Machine Learning Training - Use Your Own Inference Code with Amazon SageMaker Hosting Services

NEW QUESTION # 45
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