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Pass Guaranteed AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty Useful Free Download

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AWS MLS-C01 Exam Certification Details:

Number of Questions
65

Exam Price
$300 USD

Passing Score
750 / 1000

Duration
180 minutes

Exam Name
AWS Certified Machine Learning - Specialty (Machine Learning Specialty)

Exam Code
MLS-C01

Schedule Exam
PEARSON VUE

Sample Questions
AWS MLS-C01 Sample Questions

To become an AWS Certified Machine Learning - Specialty, you need to have a deep understanding of machine learning concepts, algorithms, and tools. You should also have practical experience in building and deploying machine learning models using AWS services such as Amazon SageMaker, AWS Lambda, Amazon Redshift, and Amazon Athena. AWS-Certified-Machine-Learning-Specialty Exam covers various topics such as data preparation, feature engineering, model training and deployment, optimization and tuning, and security and compliance. It consists of multiple-choice and multiple-response questions, and you have 170 minutes to complete it. Passing the exam requires a score of at least 750 out of 1000. By earning the AWS Certified Machine Learning - Specialty certification, you demonstrate your ability to design and deliver cutting-edge machine learning solutions on the AWS platform, which can open up new career opportunities and increase your earning potential.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q232-Q237):

NEW QUESTION # 232
A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.
Which combination of AWS services will meet these requirements?

A. AWS Data Pipeline for data transferAWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQLAmazon QuickSight for reporting and getting insights
B. AWS Glue for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQLAmazon QuickSight for reporting and getting insights
C. Amazon Kinesis Data Analytics for data ingestionAmazon EMR for data discovery, enrichment, and transformationAmazon Redshift for querying and analyzing the results in Amazon S3
D. Amazon EMR for data discovery, enrichment, and transformationAmazon Athena for querying and analyzing the results in Amazon S3 using standard SQLAmazon QuickSight for reporting and getting insights

Answer: B

Explanation:
The best combination of AWS services to meet the requirements of data discovery, enrichment, transformation, querying, analysis, and reporting with the least coding and infrastructure management is AWS Glue, Amazon Athena, and Amazon QuickSight. These services are:
* AWS Glue for data discovery, enrichment, and transformation. AWS Glue is a serverless data integration service that automatically crawls, catalogs, and prepares data from various sources and formats. It also provides a visual interface called AWS Glue DataBrew that allows users to apply over
250 transformations to clean, normalize, and enrich data without writing code1
* Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon Athena is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. It supports a variety of data formats, such as CSV, JSON, ORC, Parquet, and Avro. It also integrates with AWS Glue Data Catalog to provide a unified view of the data sources and schemas2
* Amazon QuickSight for reporting and getting insights. Amazon QuickSight is a serverless business intelligence service that allows users to create and share interactive dashboards and reports. It also provides ML-powered features, such as anomaly detection, forecasting, and natural language queries, to help users discover hidden insights from their data3 The other options are not suitable because they either require more coding effort, more infrastructure management, or do not support the desired use cases. For example:
* Option A uses Amazon EMR for data discovery, enrichment, and transformation. Amazon EMR is a managed cluster platform that runs Apache Spark, Apache Hive, and other open-source frameworks for big data processing. It requires users to write code in languages such as Python, Scala, or SQL to perform data integration tasks. It also requires users to provision, configure, and scale the clusters according to their needs4
* Option B uses Amazon Kinesis Data Analytics for data ingestion. Amazon Kinesis Data Analytics is a service that allows users to process streaming data in real time using SQL or Apache Flink. It is not suitable for data discovery, enrichment, and transformation, which are typically batch-oriented tasks. It also requires users to write code to define the data processing logic and the output destination5
* Option D uses AWS Data Pipeline for data transfer and AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation. AWS Data Pipeline is a service that helps users move data between AWS services and on-premises data sources. AWS Step Functions is a service that helps users coordinate multiple AWS services into workflows. AWS Lambda is a service that lets users run code without provisioning or managing servers. These services require users to write code to define the data sources, destinations, transformations, and workflows. They also require users to manage the scalability, performance, and reliability of the data pipelines.
1: AWS Glue - Data Integration Service - Amazon Web Services
2: Amazon Athena - Interactive SQL Query Service - AWS
3: Amazon QuickSight - Business Intelligence Service - AWS
4: Amazon EMR - Amazon Web Services
5: Amazon Kinesis Data Analytics - Amazon Web Services
AWS Data Pipeline - Amazon Web Services
AWS Step Functions - Amazon Web Services
AWS Lambda - Amazon Web Services

NEW QUESTION # 233
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test"?

A. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker
B. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the data as it is generated by Amazon SageMaker
D. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon OuickSight to visualize logs as they are being produced

Answer: C

NEW QUESTION # 234
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20
85%
B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
1312%
C. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ' 0.32
D. The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 -
0.32).

Answer: B

Explanation:
The true class frequency for Romance is the percentage of movies that are actually Romance out of all the movies. This can be calculated by dividing the sum of the true values for Romance by the total number of movies. The predicted class frequency for Adventure is the percentage of movies that are predicted to be Adventure out of all the movies. This can be calculated by dividing the sum of the predicted values for Adventure by the total number of movies. Based on the confusion matrix, the true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%. References: Confusion Matrix, Classification Metrics

NEW QUESTION # 235
A company wants to detect credit card fraud. The company has observed that an average of 2% of credit card transactions are fraudulent. A data scientist trains a classifier on a year's worth of credit card transaction dat a. The classifier needs to identify the fraudulent transactions. The company wants to accurately capture as many fraudulent transactions as possible.
Which metrics should the data scientist use to optimize the classifier? (Select TWO.)

A. Fl score
B. True positive rate
C. Accuracy
D. False positive rate
E. Specificity

Answer: A,B

Explanation:
The F1 score is a measure of the harmonic mean of precision and recall, which are both important for fraud detection. Precision is the ratio of true positives to all predicted positives, and recall is the ratio of true positives to all actual positives. A high F1 score indicates that the classifier can correctly identify fraudulent transactions and avoid false negatives. The true positive rate is another name for recall, and it measures the proportion of fraudulent transactions that are correctly detected by the classifier. A high true positive rate means that the classifier can capture as many fraudulent transactions as possible.
References:
Fraud Detection Using Machine Learning | Implementations | AWS Solutions Detect fraudulent transactions using machine learning with Amazon SageMaker | AWS Machine Learning Blog
1. Introduction - Reproducible Machine Learning for Credit Card Fraud Detection

NEW QUESTION # 236
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

A. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
B. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
C. Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
D. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Answer: A

Explanation:
AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234 The other options are not valid or secure ways to encrypt the data and protect the credit card information.
Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.

NEW QUESTION # 237
......

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