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AWS-Certified-Machine-Learning-Specialty Valid Test Materials | Reliable AWS-Certified-Machine-Learning-Specialty Test Sims

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Exam is a certification program designed to validate the skills and knowledge of individuals in the field of machine learning. AWS-Certified-Machine-Learning-Specialty exam is intended for experienced practitioners who have a deep understanding of the core principles and best practices of machine learning. It is also ideal for those who are interested in pursuing a career in machine learning and want to demonstrate their expertise to potential employers.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q88-Q93):

NEW QUESTION # 88
A data scientist needs to create a model for predictive maintenance. The model will be based on historical data to identify rare anomalies in the data.
The historical data is stored in an Amazon S3 bucket. The data scientist needs to use Amazon SageMaker Data Wrangler to ingest the data. The data scientists also needs to perform exploratory data analysis (EDA) to understand the statistical properties of the data.
Which solution will meet these requirements with the LEAST amount of compute resources?

A. Import the data by using the Stratified option.
B. Import the data by using the Randomized option. Infer the random size from domain knowledge.
C. Import the data by using the First K option. Infer the value of K from domain knowledge.
D. Import the data by using the None option.

Answer: C

Explanation:
To perform efficient exploratory data analysis (EDA) on a large dataset for anomaly detection, using the First K option in SageMaker Data Wrangler is an optimal choice. This option allows the data scientist to select the first K rows, limiting the data loaded into memory, which conserves compute resources.
Given that the First K option allows the data scientist to determine K based on domain knowledge, this approach provides a representative sample without requiring extensive compute resources. Other options like randomized sampling may not provide data samples that are as useful for initial analysis in a time-series or sequential dataset context.

NEW QUESTION # 89
A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
B. Launch the notebook instances in a public subnet and access the data through a NAT gateway
C. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.
D. Launch the notebook instances in a private subnet and access the data through a NAT gateway

Answer: C

Explanation:
A private subnet is a subnet that does not have a route to the internet gateway, which means that the resources in the private subnet cannot access the internet or be accessed from the internet. An S3 VPC endpoint is a gateway endpoint that allows the resources in the VPC to access the S3 service without going through the internet. By launching the notebook instances in a private subnet and accessing the data through an S3 VPC endpoint, the company can set up the job in a secure and compliant way, as the data never leaves the AWS network and is not exposed to the internet. This can also improve the performance and reliability of the data transfer, as the traffic does not depend on the internet bandwidth or availability.
Amazon VPC Endpoints - Amazon Virtual Private Cloud
Endpoints for Amazon S3 - Amazon Virtual Private Cloud
Connect to SageMaker Within your VPC - Amazon SageMaker
Working with VPCs and Subnets - Amazon Virtual Private Cloud

NEW QUESTION # 90
A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily.
Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

A. Insert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.
B. Spin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
C. Deploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.
D. Require that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.

Answer: A

Explanation:
Explanation

NEW QUESTION # 91
A global financial company is using machine learning to automate its loan approval process. The company has a dataset of customer information. The dataset contains some categorical fields, such as customer location by city and housing status. The dataset also includes financial fields in different units, such as account balances in US dollars and monthly interest in US cents.
The company's data scientists are using a gradient boosting regression model to infer the credit score for each customer. The model has a training accuracy of 99% and a testing accuracy of 75%. The data scientists want to improve the model's testing accuracy.
Which process will improve the testing accuracy the MOST?

A. Use tokenization of the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Remove the outliers in the data by using the z-score.
B. Use a one-hot encoder for the categorical fields in the dataset. Perform standardization on the financial fields in the dataset. Apply L1 regularization to the data.
C. Use a label encoder for the categorical fields in the dataset. Perform L1 regularization on the financial fields in the dataset. Apply L2 regularization to the data.
D. Use a logarithm transformation on the categorical fields in the dataset. Perform binning on the financial fields in the dataset. Use imputation to populate missing values in the dataset.

Answer: B

Explanation:
The question is about improving the testing accuracy of a gradient boosting regression model. The testing accuracy is much lower than the training accuracy, which indicates that the model is overfitting the training data. To reduce overfitting, the following steps are recommended:
* Use a one-hot encoder for the categorical fields in the dataset. This will create binary features for each category and avoid imposing an ordinal relationship among them. This can help the model learn the patterns better and generalize to unseen data.
* Perform standardization on the financial fields in the dataset. This will scale the features to have zero mean and unit variance, which can improve the convergence and performance of the model. This can also help the model handle features with different units and ranges.
* Apply L1 regularization to the data. This will add a penalty term to the loss function that is proportional to the absolute value of the coefficients. This can help the model reduce the complexity and select the most relevant features by shrinking the coefficients of less important features to zero.
1: AWS Machine Learning Specialty Exam Guide
2: AWS Machine Learning Specialty Course
3: AWS Machine Learning Blog

NEW QUESTION # 92
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?

A. Redeploy the model on a P3dn instance.
B. Redeploy the model as a batch transform job on an M5 instance.
C. Deploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
D. Redeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.

Answer: C

NEW QUESTION # 93
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