DETAILED MLS-C01 STUDY DUMPS | HIGH PASS-RATE AMAZON MLS-C01: AWS CERTIFIED MACHINE LEARNING - SPECIALTY

Detailed MLS-C01 Study Dumps | High Pass-Rate Amazon MLS-C01: AWS Certified Machine Learning - Specialty

Detailed MLS-C01 Study Dumps | High Pass-Rate Amazon MLS-C01: AWS Certified Machine Learning - Specialty

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Tags: Detailed MLS-C01 Study Dumps, MLS-C01 Sample Exam, MLS-C01 Reliable Test Forum, MLS-C01 Valid Test Practice, MLS-C01 Free Updates

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Achieving the AWS Certified Machine Learning - Specialty certification through the Amazon MLS-C01 Exam can demonstrate to employers and clients that you have the skills and knowledge needed to design and implement machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification can help individuals advance their careers as data scientists, machine learning engineers, and solution architects.

To qualify for this certification, you must have a solid understanding of the AWS platform and its machine learning services, as well as a working knowledge of programming languages such as Python, R, or Java. Additionally, you should have experience in designing, training, and deploying machine learning models using AWS services such as Amazon SageMaker, Amazon Comprehend, Amazon Rekognition, and Amazon Polly.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q324-Q329):

NEW QUESTION # 324
A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.
Which solution will meet these requirements with the LEAST development effort?

  • A. Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
  • B. Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
  • C. Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the metadata.
  • D. Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.

Answer: D

Explanation:
The solution that will meet the requirements with the least development effort is to use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use, assign the required metadata for each feature, and use Amazon QuickSight to analyze the metadata. This solution can leverage the existing AWS services and features to perform feature-level metadata analysis and reporting.
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. The metadata can include information such as data sensitivity, authorship, description, and parameters. The metadata can help make features discoverable, understandable, and traceable. Amazon SageMaker Feature Store allows users to set feature groups for the current features that the ML models use, and assign the required metadata for each feature using the AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), or Amazon SageMaker Studio1.
Amazon QuickSight is a fully managed, serverless business intelligence service that makes it easy to create and publish interactive dashboards that include ML insights. Amazon QuickSight can connect to various data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, and Amazon SageMaker Feature Store, and analyze the data using standard SQL or built-in ML-powered analytics. Amazon QuickSight can also create rich visualizations and reports that can be accessed from any device, and securely shared with anyone inside or outside an organization. Amazon QuickSight can be used to analyze the metadata of the features stored in Amazon SageMaker Feature Store, and generate a report that summarizes the metadata analysis2.
The other options are either more complex or less effective than the proposed solution. Using Amazon SageMaker Data Wrangler to select the features and create a data flow to perform feature-level metadata analysis would require additional steps and resources, and may not capture all the metadata attributes that the company requires. Creating an Amazon DynamoDB table to store feature-level metadata would introduce redundancy and inconsistency, as the metadata is already stored in Amazon SageMaker Feature Store. Using SageMaker Studio to analyze the metadata would not generate a report that can be easily shared and accessed by the company.
References:
1: Amazon SageMaker Feature Store - Amazon Web Services
2: Amazon QuickSight - Business Intelligence Service - Amazon Web Services


NEW QUESTION # 325
An ecommerce company wants to use machine learning (ML) to monitor fraudulent transactions on its website. The company is using Amazon SageMaker to research, train, deploy, and monitor the ML models.
The historical transactions data is in a .csv file that is stored in Amazon S3 The data contains features such as the user's IP address, navigation time, average time on each page, and the number of clicks for ....session. There is no label in the data to indicate if a transaction is anomalous.
Which models should the company use in combination to detect anomalous transactions? (Select TWO.)

  • A. IP Insights
  • B. Random Cut Forest (RCF)
  • C. XGBoost
  • D. K-nearest neighbors (k-NN)
  • E. Linear learner with a logistic function

Answer: B,C

Explanation:
To detect anomalous transactions, the company can use a combination of Random Cut Forest (RCF) and XGBoost models. RCF is an unsupervised algorithm that can detect outliers in the data by measuring the depth of each data point in a collection of random decision trees. XGBoost is a supervised algorithm that can learn from the labeled data points generated by RCF and classify them as normal or anomalous. RCF can also provide anomaly scores that can be used as features for XGBoost to improve the accuracy of the classification. References:
1: Amazon SageMaker Random Cut Forest
2: Amazon SageMaker XGBoost Algorithm
3: Anomaly Detection with Amazon SageMaker Random Cut Forest and Amazon SageMaker XGBoost


NEW QUESTION # 326
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

  • A. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
  • B. The precision of the model is 86%, which is less than the accuracy of the model.
  • C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
  • D. The precision of the model is 86%, which is greater than the accuracy of the model.

Answer: A


NEW QUESTION # 327
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
- Combine multiple data sources.
- Reuse existing PySpark logic.
- Run the solution on the existing schedule.
- Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?

  • A. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
  • B. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a "processed" location in Amazon S3 that is accessible for downstream use.
  • C. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use.
  • D. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.

Answer: C

Explanation:
Kinesis Data Analytics can not directly stream the input data.


NEW QUESTION # 328
A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network.
How should the Data Science team configure the notebook instance placement to meet these requirements?

  • A. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
  • B. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
  • C. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
  • D. Associate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.

Answer: C

Explanation:
We must use the VPC endpoint (either Gateway Endpoint or Interface Endpoint)to comply with this requirement "Data communication traffic must stay within the AWS network".
https://docs.aws.amazon.com/sagemaker/latest/dg/notebook-interface-endpoint.html


NEW QUESTION # 329
......

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