FREE PDF QUIZ AMAZON - MLS-C01 - AWS CERTIFIED MACHINE LEARNING - SPECIALTY–HIGH PASS-RATE RELIABLE TEST DURATION

Free PDF Quiz Amazon - MLS-C01 - AWS Certified Machine Learning - Specialty–High Pass-Rate Reliable Test Duration

Free PDF Quiz Amazon - MLS-C01 - AWS Certified Machine Learning - Specialty–High Pass-Rate Reliable Test Duration

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q120-Q125):

NEW QUESTION # 120
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

  • A. Enable early stopping
  • B. Choose a higher number of layers
  • C. Choose a lower number of layers
  • D. Include all the images from the test set in the training set
  • E. Enable dropout
  • F. Choose a smaller learning rate

Answer: A,C,E

Explanation:
The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:
Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.
Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.
Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing. This prevents the model from overtraining on the training data and reduces overfitting.
References:
Deep Learning - Machine Learning Lens
How to Avoid Overfitting in Deep Learning Neural Networks
How to Identify Overfitting Machine Learning Models in Scikit-Learn


NEW QUESTION # 121
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)

  • A. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
  • B. Change the XGBoost evaljnetric parameter to optimize based on AUC instead of error.
  • C. Change the XGBoost eval_metric parameter to optimize based on rmse instead of error.
  • D. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
  • E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer: B,E


NEW QUESTION # 122
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?

  • A. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
  • B. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
  • C. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
  • D. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.

Answer: B

Explanation:
Explanation
Using AWS Glue to catalogue the data and Amazon Athena to run queries is the solution that requires the least effort to be able to query the data stored in an Amazon S3 bucket using SQL. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can automatically discover, crawl, and catalogue the data stored in various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, etc. AWS Glue can also use AWS KMS to encrypt the data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can handle both structured and unstructured data, and support various data formats, such as CSV, JSON, Parquet, etc. AWS Glue can also use built-in or custom classifiers to identify and parse the data schema and format1 Amazon Athena is a service that provides an interactive query engine that can run SQL queries directly on data stored in Amazon S3. Amazon Athena can integrate with AWS Glue to use the Glue Data Catalog as a central metadata repository for the data sources and tables.
Amazon Athena can also use AWS KMS to encrypt the data at rest on Amazon S3 and the query results.
Amazon Athena can query both structured and unstructured data, and support various data formats, such as CSV, JSON, Parquet, etc. Amazon Athena can also use partitions and compression to optimize the query performance and reduce the query cost23 The other options are not valid or require more effort to query the data stored in an Amazon S3 bucket using SQL. Using AWS Data Pipeline to transform the data and Amazon RDS to run queries is not a good option, as it involves moving the data from Amazon S3 to Amazon RDS, which can incur additional time and cost. AWS Data Pipeline is a service that can orchestrate and automate data movement and transformation across various AWS services and on-premises data sources. AWS Data Pipeline can be integrated with Amazon EMR to run ETL jobs on the data stored in Amazon S3. Amazon RDS is a service that provides a managed relational database service that can run various database engines, such as MySQL, PostgreSQL, Oracle, etc. Amazon RDS can use AWS KMS to encrypt the data at rest and in transit. Amazon RDS can run SQL queries on the data stored in the database tables45 Using AWS Batch to run ETL on the data and Amazon Aurora to run the queries is not a good option, as it also involves moving the data from Amazon S3 to Amazon Aurora, which can incur additional time and cost. AWS Batch is a service that can run batch computing workloads on AWS.
AWS Batch can be integrated with AWS Lambda to trigger ETL jobs on the data stored in Amazon S3.
Amazon Aurora is a service that provides a compatible and scalable relational database engine that can run MySQL or PostgreSQL. Amazon Aurora can use AWS KMS to encrypt the data at rest and in transit. Amazon Aurora can run SQL queries on the data stored in the database tables. Using AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries is not a good option, as it is not suitable for querying data stored in Amazon S3 using SQL. AWS Lambda is a service that can run serverless functions on AWS.
AWS Lambda can be integrated with Amazon S3 to trigger data transformation functions on the data stored in Amazon S3. Amazon Kinesis Data Analytics is a service that can analyze streaming data using SQL or Apache Flink. Amazon Kinesis Data Analytics can be integrated with Amazon Kinesis Data Streams or Amazon Kinesis Data Firehose to ingest streaming data sources, such as web logs, social media, IoT devices, etc. Amazon Kinesis Data Analytics is not designed for querying data stored in Amazon S3 using SQL.


NEW QUESTION # 123
A Data Scientist needs to create a serverless ingestion and analytics solution for high-velocity, real-time streaming data.
The ingestion process must buffer and convert incoming records from JSON to a query-optimized, columnar format without data loss. The output datastore must be highly available, and Analysts must be able to run SQL queries against the data and connect to existing business intelligence dashboards.
Which solution should the Data Scientist build to satisfy the requirements?

  • A. Use Amazon Kinesis Data Analytics to ingest the streaming data and perform real-time SQL queries to convert the records to Apache Parquet before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
  • B. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and writes the data to a processed data location in Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
  • C. Create a schema in the AWS Glue Data Catalog of the incoming data format. Use an Amazon Kinesis Data Firehose delivery stream to stream the data and transform the data to Apache Parquet or ORC format using the AWS Glue Data Catalog before delivering to Amazon S3. Have the Analysts query the data directly from Amazon S3 using Amazon Athena, and connect to Bl tools using the Athena Java Database Connectivity (JDBC) connector.
  • D. Write each JSON record to a staging location in Amazon S3. Use the S3 Put event to trigger an AWS Lambda function that transforms the data into Apache Parquet or ORC format and inserts it into an Amazon RDS PostgreSQL database. Have the Analysts query and run dashboards from the RDS database.

Answer: A


NEW QUESTION # 124
A company deployed a machine learning (ML) model on the company website to predict real estate prices. Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased.
The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues.
Which solution will meet these requirements?

  • A. Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyper para meters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.
  • B. Use only data from the previous several months to perform incremental training to update the model. Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
  • C. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team Retrain the model by using only data from the previous several months.
  • D. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.

Answer: D

Explanation:
The best solution to improve the accuracy of the model and receive notifications for any future performance issues is to perform incremental training to update the model and activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications. Incremental training is a technique that allows you to update an existing model with new data without retraining the entire model from scratch. This can save time and resources, and help the model adapt to changing data patterns. Amazon SageMaker Model Monitor is a feature that continuously monitors the quality of machine learning models in production and notifies you when there are deviations in the model quality, such as data drift and anomalies. You can set up alerts that trigger actions, such as sending notifications to Amazon Simple Notification Service (Amazon SNS) topics, when certain conditions are met.
Option B is incorrect because Amazon SageMaker Model Governance is a set of tools that help you implement ML responsibly by simplifying access control and enhancing transparency. It does not provide a mechanism to automatically adjust model hyperparameters or improve model accuracy.
Option C is incorrect because Amazon SageMaker Debugger is a feature that helps you debug and optimize your model training process by capturing relevant data and providing real-time analysis. However, using Debugger alone does not update the model or monitor its performance in production. Also, retraining the model by using only data from the previous several months may not capture the full range of data variability and may introduce bias or overfitting.
Option D is incorrect because using only data from the previous several months to perform incremental training may not be sufficient to improve the model accuracy, as explained above. Moreover, this option does not specify how to activate Amazon SageMaker Model Monitor or configure the alerts and notifications.
References:
Incremental training
Amazon SageMaker Model Monitor
Amazon SageMaker Model Governance
Amazon SageMaker Debugger


NEW QUESTION # 125
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