EXAM QUESTIONS AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY VCE & AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY REGUALER UPDATE

Exam Questions AWS-Certified-Machine-Learning-Specialty Vce & AWS-Certified-Machine-Learning-Specialty Regualer Update

Exam Questions AWS-Certified-Machine-Learning-Specialty Vce & AWS-Certified-Machine-Learning-Specialty Regualer Update

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed for individuals who want to validate their expertise in ML on AWS. AWS Certified Machine Learning - Specialty certification is ideal for data scientists and machine learning developers who want to demonstrate their skills in building, training, deploying, and managing ML models on AWS. Candidates must have prior experience in ML and have completed relevant training or have equivalent practical experience. Passing AWS-Certified-Machine-Learning-Specialty Exam requires a deep understanding of ML concepts and practical experience using AWS services and tools.

The AWS Certified Machine Learning - Specialty certification is an important credential for professionals who work with machine learning technologies on the AWS platform. The Amazon MLS-C01 exam is designed to test candidates' knowledge and skills in machine learning algorithms, AWS services, and data management. Preparing for the exam requires a strong understanding of machine learning concepts and AWS services, as well as practice with sample questions and exams.

>> Exam Questions AWS-Certified-Machine-Learning-Specialty Vce <<

100% Pass 2025 Amazon Marvelous AWS-Certified-Machine-Learning-Specialty: Exam Questions AWS Certified Machine Learning - Specialty Vce

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Amazon MLS-C01 certification exam consists of 65 multiple-choice and multiple-response questions and has a duration of 180 minutes. It is a challenging exam that requires extensive knowledge and experience in machine learning concepts and technologies. Candidates are required to have a thorough understanding of AWS services and how to use them to build and deploy machine learning models.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q253-Q258):

NEW QUESTION # 253
A data scientist uses Amazon SageMaker Data Wrangler to define and perform transformations and feature engineering on historical data. The data scientist saves the transformations to SageMaker Feature Store.
The historical data is periodically uploaded to an Amazon S3 bucket. The data scientist needs to transform the new historic data and add it to the online feature store The data scientist needs to prepare the .....historic data for training and inference by using native integrations.
Which solution will meet these requirements with the LEAST development effort?

  • A. Run an AWS Step Functions step and a predefined SageMaker pipeline to perform the transformations on each new dalaset that arrives in the S3 bucket
  • B. Use AWS Lambda to run a predefined SageMaker pipeline to perform the transformations on each new dataset that arrives in the S3 bucket.
  • C. Configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket.
  • D. Use Apache Airflow to orchestrate a set of predefined transformations on each new dataset that arrives in the S3 bucket.

Answer: C

Explanation:
Explanation
The best solution is to configure Amazon EventBridge to run a predefined SageMaker pipeline to perform the transformations when a new data is detected in the S3 bucket. This solution requires the least development effort because it leverages the native integration between EventBridge and SageMaker Pipelines, which allows you to trigger a pipeline execution based on an event rule. EventBridge can monitor the S3 bucket for new data uploads and invoke the pipeline that contains the same transformations and feature engineering steps that were defined in SageMaker Data Wrangler. The pipeline can then ingest the transformed data into the online feature store for training and inference.
The other solutions are less optimal because they require more development effort and additional services.
Using AWS Lambda or AWS Step Functions would require writing custom code to invoke the SageMaker pipeline and handle any errors or retries. Using Apache Airflow would require setting up and maintaining an Airflow server and DAGs, as well as integrating with the SageMaker API.
References:
Amazon EventBridge and Amazon SageMaker Pipelines integration
Create a pipeline using a JSON specification
Ingest data into a feature group


NEW QUESTION # 254
A manufacturing company wants to create a machine learning (ML) model to predict when equipment is likely to fail. A data science team already constructed a deep learning model by using TensorFlow and a custom Python script in a local environment. The company wants to use Amazon SageMaker to train the model.
Which TensorFlow estimator configuration will train the model MOST cost-effectively?

  • A. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Turn on managed spot training by setting the use_spot_instances parameter to True. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • B. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Set the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • C. Turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.
  • D. Adjust the training script to use distributed data parallelism. Specify appropriate values for the distribution parameter. Pass the script to the estimator in the call to the TensorFlow fit() method.

Answer: A

Explanation:
The TensorFlow estimator configuration that will train the model most cost-effectively is to turn on SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter, turn on managed spot training by setting the use_spot_instances parameter to True, and pass the script to the estimator in the call to the TensorFlow fit() method. This configuration will optimize the model for the target hardware platform, reduce the training cost by using Amazon EC2 Spot Instances, and use the custom Python script without any modification.
SageMaker Training Compiler is a feature of Amazon SageMaker that enables you to optimize your TensorFlow, PyTorch, and MXNet models for inference on a variety of target hardware platforms. SageMaker Training Compiler can improve the inference performance and reduce the inference cost of your models by applying various compilation techniques, such as operator fusion, quantization, pruning, and graph optimization. You can enable SageMaker Training Compiler by adding compiler_config=TrainingCompilerConfig() as a parameter to the TensorFlow estimator constructor1.
Managed spot training is another feature of Amazon SageMaker that enables you to use Amazon EC2 Spot Instances for training your machine learning models. Amazon EC2 Spot Instances let you take advantage of unused EC2 capacity in the AWS Cloud. Spot Instances are available at up to a 90% discount compared to On-Demand prices. You can use Spot Instances for various fault-tolerant and flexible applications. You can enable managed spot training by setting the use_spot_instances parameter to True and specifying the max_wait and max_run parameters in the TensorFlow estimator constructor2.
The TensorFlow estimator is a class in the SageMaker Python SDK that allows you to train and deploy TensorFlow models on SageMaker. You can use the TensorFlow estimator to run your own Python script on SageMaker, without any modification. You can pass the script to the estimator in the call to the TensorFlow fit() method, along with the location of your input data. The fit() method starts a SageMaker training job and runs your script as the entry point in the training containers3.
The other options are either less cost-effective or more complex to implement. Adjusting the training script to use distributed data parallelism would require modifying the script and specifying appropriate values for the distribution parameter, which could increase the development time and complexity. Setting the MaxWaitTimeInSeconds parameter to be equal to the MaxRuntimeInSeconds parameter would not reduce the cost, as it would only specify the maximum duration of the training job, regardless of the instance type.
References:
1: Optimize TensorFlow, PyTorch, and MXNet models for deployment using Amazon SageMaker Training Compiler | AWS Machine Learning Blog
2: Managed Spot Training: Save Up to 90% On Your Amazon SageMaker Training Jobs | AWS Machine Learning Blog
3: sagemaker.tensorflow - sagemaker 2.66.0 documentation


NEW QUESTION # 255
A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.
The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpoint Which combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

  • A. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,
  • B. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.
  • C. Include an inline policy for the data scientist's 1AM user that allows SageMaker to read S3 objects
  • D. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.
  • E. Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.
  • F. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query.

Answer: A,C,F

Explanation:
The correct combination of actions to enable the data scientist's IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:
B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.
C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.
E: Including the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.
The other options are not correct or necessary, because they have the following drawbacks:
A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.
D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.
F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.
References:
1: InvokeEndpoint - Amazon SageMaker
2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena
3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog
4: AmazonAthenaFullAccess - AWS Identity and Access Management
5: GetRecord - Amazon SageMaker Feature Store Runtime
6: [Invoke a Multi-Model Endpoint - Amazon SageMaker]


NEW QUESTION # 256
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 Glue to catalogue the data and Amazon Athena to run queries.
  • B. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
  • C. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
  • D. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Answer: C


NEW QUESTION # 257
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. Recall
  • B. Root Mean Square Error (RMSE)
  • C. Precision
  • D. Area Under the ROC Curve (AUC)

Answer: D

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.
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 # 258
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