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Pass Guaranteed Quiz Amazon - Newest MLS-C01 - New AWS Certified Machine Learning - Specialty Practice Materials

Pass Guaranteed Quiz Amazon - Newest MLS-C01 - New AWS Certified Machine Learning - Specialty Practice Materials

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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Exam is a certification program that is designed to test and validate the skills and knowledge of individuals who are interested in machine learning. MLS-C01 Exam is intended for individuals who already have a foundational understanding of AWS services and machine learning concepts. AWS Certified Machine Learning - Specialty certification is suitable for data scientists, software developers, and IT professionals who want to showcase their expertise in machine learning and AWS.

Certification Path

There is no prerequisite for AWS Certified Machine Learning Specialty exam.

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Career Opportunities

Machine Learning is no doubt one of the hottest topics within the Information Technology sector. Therefore, the Amazon AWS Certified Machine Learning – Specialty certification is simply the key to become a highly regarded certified professional in the field. Those professionals who obtain this certificate can boost their career to a higher level and get a decent salary. They can opt for different job roles, such as a Solutions Architect, a Technical Curriculum Developer, an Electrical Safety Program Manager, a Systems Development Engineer, a Software Development Manager, a Global Ergonomics Engineer, and many more. The average salary can range from $30,000 to $160,000 per year.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q61-Q66):

NEW QUESTION # 61
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?

  • A. Root Mean Square Error (RMSE)
  • B. Area Under the ROC Curve (AUC)
  • C. Precision
  • D. Recall

Answer: B

Explanation:
Area Under the ROC Curve (AUC) is a metric that is best suited to score the model for the given scenario. AUC is a measure of the performance of a binary classifier, such as a model that predicts whether a credit card transaction is valid or fraudulent. AUC is calculated based on the Receiver Operating Characteristic (ROC) curve, which is a plot that shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of the classifier as the decision threshold is varied. The TPR, also known as recall or sensitivity, is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. The FPR, also known as the fall-out, is the proportion of actual negative cases (valid transactions) that are incorrectly predicted as positive by the classifier. The ROC curve illustrates how well the classifier can distinguish between the two classes, regardless of the class distribution or the error costs. A perfect classifier would have a TPR of 1 and an FPR of 0 for all thresholds, resulting in a ROC curve that goes from the bottom left to the top left and then to the top right of the plot. A random classifier would have a TPR and an FPR that are equal for all thresholds, resulting in a ROC curve that goes from the bottom left to the top right of the plot along the diagonal line. AUC is the area under the ROC curve, and it ranges from 0 to 1. A higher AUC indicates a better classifier, as it means that the classifier has a higher TPR and a lower FPR for all thresholds. AUC is a useful metric for imbalanced classification problems, such as the credit card transaction dataset, because it is insensitive to the class imbalance and the error costs. AUC can capture the overall performance of the classifier across all possible scenarios, and it can be used to compare different classifiers based on their ROC curves.
The other options are not as suitable as AUC for the given scenario for the following reasons:
Precision: Precision is the proportion of predicted positive cases (fraudulent transactions) that are actually positive. Precision is a useful metric when the cost of a false positive is high, such as in spam detection or medical diagnosis. However, precision is not a good metric for imbalanced classification problems, because it can be misleadingly high when the positive class is rare. For example, a classifier that predicts all transactions as valid would have a precision of 0, but a very high accuracy of 99%. Precision is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Recall: Recall is the same as the TPR, and it is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. Recall is a useful metric when the cost of a false negative is high, such as in fraud detection or cancer diagnosis. However, recall is not a good metric for imbalanced classification problems, because it can be misleadingly low when the positive class is rare. For example, a classifier that predicts all transactions as fraudulent would have a recall of 1, but a very low accuracy of 1%. Recall is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Root Mean Square Error (RMSE): RMSE is a metric that measures the average difference between the predicted and the actual values. RMSE is a useful metric for regression problems, where the goal is to predict a continuous value, such as the price of a house or the temperature of a city. However, RMSE is not a good metric for classification problems, where the goal is to predict a discrete value, such as the class label of a transaction. RMSE is not meaningful for classification problems, because it does not capture the accuracy or the error costs of the predictions.
References:
ROC Curve and AUC
How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python Precision-Recall Root Mean Squared Error


NEW QUESTION # 62
A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.
A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations.
Which AWS service or feature will meet these requirements with the LEAST development effort over time?

  • A. AWS Lambda
  • B. AWS Glue jobs
  • C. Amazon Athena
  • D. Amazon EMR cluster

Answer: B

Explanation:
AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating dat a. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1.
The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.
References:
1: AWS Glue - Fully Managed ETL Service - Amazon Web Services
2: Amazon EMR - Amazon Web Services
3: Amazon Athena - Interactive SQL Queries for Data in Amazon S3
[4]: AWS Lambda - Serverless Compute - Amazon Web Services


NEW QUESTION # 63
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

  • A. Include a copy of the samples in the test dataset in the training dataset.
  • B. Change the cost function so that false negatives have a higher impact on the cost value than false positives.
  • C. Add more deep trees to the random forest to enable the model to learn more features.
  • D. Change the cost function so that false positives have a higher impact on the cost value than false negatives.
  • E. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

Answer: B,E


NEW QUESTION # 64
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?

  • A. AWS Glue with a custom ETL script to transform the data.
  • B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
  • C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
  • D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.

Answer: B

Explanation:
Explanation
Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.
To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.
The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.
References:
What Is Amazon Kinesis Data Analytics for SQL Applications?
Using AWS Lambda with Amazon Kinesis Data Streams
Using AWS Lambda with Amazon Kinesis Data Firehose


NEW QUESTION # 65
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 for 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 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.
  • B. Use Amazon Kinesis Data Analytics to stream the input data and perform realtime 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 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 PySpartc 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
  • D. 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 m Amazon S3 that is accessible tor downstream use

Answer: A

Explanation:
* The Data Scientist needs to migrate an existing on-premises ETL process to the cloud, using a solution that can combine multiple data sources, reuse existing PySpark logic, run on the existing schedule, and minimize the number of servers that need to be managed. The best architecture for this scenario is to use AWS Glue, which is a serverless data integration service that can create and run ETL jobs on AWS.
* AWS Glue can perform the following tasks to meet the requirements:
* Combine multiple data sources: AWS Glue can access data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon DynamoDB, and more. AWS Glue can also crawl the data sources and discover their schemas, formats, and partitions, and store them in the AWS Glue Data Catalog, which is a centralized metadata repository for all the data assets.
* Reuse existing PySpark logic: AWS Glue supports writing ETL scripts in Python or Scala, using Apache Spark as the underlying execution engine. AWS Glue provides a library of built-in transformations and connectors that can simplify the ETL code. The Data Scientist can write the ETL job in PySpark and leverage the existing logic to perform the data processing.
* Run the solution on the existing schedule: AWS Glue can create triggers that can start ETL jobs based on a schedule, an event, or a condition. The Data Scientist can create a new AWS Glue trigger to run the ETL job based on the existing schedule, using a cron expression or a relative time interval.
* Minimize the number of servers that need to be managed: AWS Glue is a serverless service, which means that it automatically provisions, configures, scales, and manages the compute resources required to run the ETL jobs. The Data Scientist does not need to worry about setting up, maintaining, or monitoring any servers or clusters for the ETL process.
* Therefore, the Data Scientist should use the following architecture to build the cloud solution:
* Write the raw data to Amazon S3: The Data Scientist can use any method to upload the raw data from the on-premises sources to Amazon S3, such as AWS DataSync, AWS Storage Gateway, AWS Snowball, or AWS Direct Connect. Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data.
* Create an AWS Glue ETL job to perform the ETL processing against the input data: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue ETL job. The Data Scientist can specify the input and output data sources, the IAM role, the security configuration, the job parameters, and the PySpark script location. The Data Scientist can also use the AWS Glue Studio, which is a graphical interface that can help design, run, and monitor ETL jobs visually.
* Write the ETL job in PySpark to leverage the existing logic: The Data Scientist can use a code editor of their choice to write the ETL script in PySpark, using the existing logic to transform the data. The Data Scientist can also use the AWS Glue script editor, which is an integrated development environment (IDE) that can help write, debug, and test the ETL code. The Data Scientist can store the ETL script in Amazon S3 or GitHub, and reference it in the AWS Glue ETL job configuration.
* Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue trigger. The Data Scientist can specify the name, type, and schedule of the trigger, and associate it with the AWS Glue ETL job. The trigger will start the ETL job according to the defined schedule.
* Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use: The Data Scientist can specify the output location of the ETL job in the PySpark script, using the AWS Glue DynamicFrame or Spark DataFrame APIs. The Data Scientist can write the output data to a "processed" location in Amazon S3, using a format such as Parquet, ORC, JSON, or CSV, that is suitable for downstream processing.
References:
* What Is AWS Glue?
* AWS Glue Components
* AWS Glue Studio
* AWS Glue Triggers


NEW QUESTION # 66
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