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100% Pass Efficient AWS-Certified-Machine-Learning-Specialty - Latest AWS Certified Machine Learning - Specialty Exam Guide

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To be eligible for the AWS Certified Machine Learning - Specialty exam, candidates must have a minimum of one year of experience in developing and deploying machine learning models using AWS services. They should also have a strong understanding of machine learning algorithms and techniques, as well as experience with programming languages such as Python and R. AWS-Certified-Machine-Learning-Specialty Exam consists of 65 multiple-choice and multiple-response questions, and candidates have 180 minutes to complete it.

What Topics Are Covered in AWS Machine Learning - Specialty Certification Exam?

The certification exam for the AWS Machine Learning – Specialty certification tests the candidates' ability to select the best machine learning strategy to improve the business processes. Also, they will be able to identify the best AWS service needed to implement different machine learning solutions. Besides, candidates will be able to design and put into practice reliable, scalable, and cost-optimized machine learning solutions. The AWS MLS-C01 exam, in particular, focuses on four domains. They are the following:

Exploratory Data Analysis;
Machine Learning Implementation and Operations.
Modeling;
Data engineering;

The first topic handles data engineering and has 3 sections. The first one handles the creation of data repositories for efficient machine learning strategies. Also, candidates will learn how to effectively identify and implement solutions related to data-ingestion. The third sub-domain included in this section focuses on the implementation and identification of different data-transformation solutions.

The second tested area concentrates on exploratory data analysis. When they prepare for this topic, candidates will learn how to sanitize and prepare data for modeling. Also, they will learn how to make a performance when it comes to feature engineering. Finally, candidates will learn how to visualize data and analyze different parameters for machine learning.

Within the modeling section, candidates will learn how to frame different business problems related to machine learning issues. Besides, they will find how to select the right models for different machine learning problems. In this section, candidates will also learn how to train effectively for machine learning models. Another essential concept that candidates will discover in this part is related to hyperparameter optimization performance. Last but not least, applicants will understand how to correctly evaluate machine learning models.

The final domain concentrates on machine learning operations and implementation. This segment focuses on helping candidates develop advanced abilities in building machine learning solutions to achieve the highest performance and fault tolerance. These solutions will help them ensure availability, resilience, and scalability. Another subtopic included in the last objective deals with the recommendations and implementation of the right machine learning services adapted to the business context. Candidates will also learn how to apply fundamental AWS security practices to solve different machine learning issues. Finally, they will become ready to deploy and operationalize various machine learning solutions.

The AWS Certified Machine Learning - Specialty certification exam is a challenging and prestigious certification that is designed for individuals who want to demonstrate their skills in building, training, and deploying machine learning models using Amazon Web Services (AWS). AWS Certified Machine Learning - Specialty certification validates the candidate's ability to design, implement, and maintain machine learning solutions for various business applications.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q50-Q55):

NEW QUESTION # 50
A retail company is ingesting purchasing records from its network of 20,000 stores to Amazon S3 by using Amazon Kinesis Data Firehose. The company uses a small, server-based application in each store to send the data to AWS over the internet. The company uses this data to train a machine learning model that is retrained each day. The company's data science team has identified existing attributes on these records that could be combined to create an improved model.
Which change will create the required transformed records with the LEAST operational overhead?

A. Deploy an Amazon S3 File Gateway in the stores. Update the in-store software to deliver data to the S3 File Gateway. Use a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3.
B. Launch a fleet of Amazon EC2 instances that include the transformation logic. Configure the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.
C. Deploy an Amazon EMR cluster that runs Apache Spark and includes the transformation logic. Use Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3. Deliver the transformed records to Amazon S3.
D. Create an AWS Lambda function that can transform the incoming records. Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target.

Answer: D

Explanation:
The solution A will create the required transformed records with the least operational overhead because it uses AWS Lambda and Amazon Kinesis Data Firehose, which are fully managed services that can provide the desired functionality. The solution A involves the following steps:
Create an AWS Lambda function that can transform the incoming records. AWS Lambda is a service that can run code without provisioning or managing servers. AWS Lambda can execute the transformation logic on the purchasing records and add the new attributes to the records1.
Enable data transformation on the ingestion Kinesis Data Firehose delivery stream. Use the Lambda function as the invocation target. Amazon Kinesis Data Firehose is a service that can capture, transform, and load streaming data into AWS data stores. Amazon Kinesis Data Firehose can enable data transformation and invoke the Lambda function to process the incoming records before delivering them to Amazon S3. This can reduce the operational overhead of managing the transformation process and the data storage2.
The other options are not suitable because:
Option B: Deploying an Amazon EMR cluster that runs Apache Spark and includes the transformation logic, using Amazon EventBridge (Amazon CloudWatch Events) to schedule an AWS Lambda function to launch the cluster each day and transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the Amazon EMR cluster, the Apache Spark application, the AWS Lambda function, and the Amazon EventBridge rule. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day3.
Option C: Deploying an Amazon S3 File Gateway in the stores, updating the in-store software to deliver data to the S3 File Gateway, and using a scheduled daily AWS Glue job to transform the data that the S3 File Gateway delivers to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the S3 File Gateway, the in-store software, and the AWS Glue job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day4.
Option D: Launching a fleet of Amazon EC2 instances that include the transformation logic, configuring the EC2 instances with a daily cron job to transform the records that accumulate in Amazon S3, and delivering the transformed records to Amazon S3 will incur more operational overhead than using AWS Lambda and Amazon Kinesis Data Firehose. The company will have to manage the EC2 instances, the transformation code, and the cron job. Moreover, this solution will introduce a delay in the transformation process, as it will run only once a day5.
References:
1: AWS Lambda
2: Amazon Kinesis Data Firehose
3: Amazon EMR
4: Amazon S3 File Gateway
5: Amazon EC2

NEW QUESTION # 51
A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings.
To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high-speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities.
Which deployment architecture for the model will address these business requirements?

A. Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
B. Deploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table.Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
C. Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
D. Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.

Answer: C

Explanation:
https://aws.amazon.com/blogs/iot/industrial-iot-from-condition-based-monitoring-to-predictive-quality-to-digitize-your-factory-with-aws-iot-services/
https://aws.amazon.com/blogs/iot/using-aws-iot-for-predictive-maintenance/

NEW QUESTION # 52
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?

A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
B. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
C. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: B

Explanation:
The best services to use for building a data ingestion solution for the company's Amazon S3-based data lake are:
* AWS Glue as the data catalog: AWS Glue is a fully managed extract, transform, and load (ETL) service that can discover, crawl, and catalog data from various sources and formats, and make it available for analysis. AWS Glue can also generate ETL code in Python or Scala to transform, enrich, and join data using AWS Glue Data Catalog as the metadata repository. AWS Glue Data Catalog is a central metadata store that integrates with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum, allowing users to create a unified view of their data across various sources and formats.
* Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights: Amazon Kinesis Data Streams is a service that enables users to collect, process, and analyze real-time streaming data at any scale. Users can create data streams that can capture data from various sources, such as web and mobile applications, IoT devices, and social media platforms. Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data using standard SQL queries or Apache Flink applications. Users can create real-time dashboards, metrics, and alerts based on the streaming data analysis results.
* Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics: Amazon Kinesis Data Firehose is a service that enables users to load streaming data into data lakes, data stores, and analytics services. Users can configure Kinesis Data Firehose to automatically deliver data to various destinations, such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and third-party solutions. For clickstream analytics, users can use Kinesis Data Firehose to deliver data to Amazon OpenSearch Service, a fully managed service that offers search and analytics capabilities for log data.
Users can use Amazon OpenSearch Service to perform interactive analysis and visualization of clickstream data using Kibana, an open-source tool that is integrated with Amazon OpenSearch Service.
* Amazon EMR to generate personalized product recommendations: Amazon EMR is a service that enables users to run distributed data processing frameworks, such as Apache Spark, Apache Hadoop, and Apache Hive, on scalable clusters of EC2 instances. Users can use Amazon EMR to perform advanced analytics, such as machine learning, on large and complex datasets stored in Amazon S3 or other sources. For product recommendations, users can use Amazon EMR to run Spark MLlib, a library that provides scalable machine learning algorithms, such as collaborative filtering, to generate personalized recommendations based on user behavior and preferences.
References:
* AWS Glue - Fully Managed ETL Service
* Amazon Kinesis - Data Streaming Service
* Amazon OpenSearch Service - Managed OpenSearch Service
* Amazon EMR - Managed Hadoop Framework

NEW QUESTION # 53
A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

A. Reduce the number of layers and units (or neurons) from the deep learning network.
B. Apply L1 or L2 regularization and dropouts to the training.
C. Allocate a higher proportion of the overall data to the training dataset
D. Increase the randomization of training data in the mini-batches used in training.

Answer: B

Explanation:
Regularization and dropouts are techniques that can help reduce overfitting in deep learning models.
Overfitting occurs when the model learns too much from the training data and fails to generalize well to new data. Regularization adds a penalty term to the loss function that penalizes the model for having large or complex weights. This prevents the model from memorizing the noise or irrelevant features in the training data. L1 and L2 are two types of regularization that differ in how they calculate the penalty term. L1 regularization uses the absolute value of the weights, while L2 regularization uses the square of the weights.
Dropouts are another technique that randomly drops out some units or neurons from the network during training. This creates a thinner network that is less prone to overfitting. Dropouts also act as a form of ensemble learning, where multiple sub-models are combined to produce a better prediction. By applying regularization and dropouts to the training, the web-based company can improve the generalization and accuracy of its deep learning model on the test and validation data. References:
* Regularization: A video that explains the concept and benefits of regularization in deep learning.
* Dropout: A video that demonstrates how dropout works and why it helps reduce overfitting.

NEW QUESTION # 54
A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1 000 records and 50 features Prior to training, the ML Specialist notices that two features are perfectly linearly dependent Why could this be an issue for the linear least squares regression model?

A. It could modify the loss function during optimization causing it to fail during training
B. It could introduce non-linear dependencies within the data which could invalidate the linear assumptions of the model
C. It could cause the backpropagation algorithm to fail during training
D. It could create a singular matrix during optimization which fails to define a unique solution

Answer: D

Explanation:
Linear least squares regression is a method of fitting a linear model to a set of data by minimizing the sum of squared errors between the observed and predicted values. The solution of the linear least squares problem can be obtained by solving the normal equations, which are given by ATAx=ATb, where A is the matrix of explanatory variables, b is the vector of response variables, and x is the vector of unknown coefficients.
However, if the matrix A has two features that are perfectly linearly dependent, then the matrix ATA will be singular, meaning that it does not have a unique inverse. This implies that the normal equations do not have a unique solution, and the linear least squares problem is ill-posed. In other words, there are infinitely many values of x that can satisfy the normal equations, and the linear model is not identifiable.
This can be an issue for the linear least squares regression model, as it can lead to instability, inconsistency, and poor generalization of the model. It can also cause numerical difficulties when trying to solve the normal equations using computational methods, such as matrix inversion or decomposition. Therefore, it is advisable to avoid or remove the linearly dependent features from the matrix A before applying the linear least squares regression model.
References:
Linear least squares (mathematics)
Linear Regression in Matrix Form
Singular Matrix Problem

NEW QUESTION # 55
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