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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is a specialized exam designed for individuals who want to validate their ability to design, implement, deploy, and maintain machine learning (ML) solutions on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification is ideal for professionals who have experience in ML and want to showcase their skills and knowledge in this area. AWS-Certified-Machine-Learning-Specialty exam is intended for individuals who have a deep understanding of ML frameworks, algorithms, and AWS services, and want to demonstrate their expertise to potential employers and clients.

The AWS-Certified-Machine-Learning-Specialty Exam is a challenging certification program that requires a comprehensive understanding of machine learning concepts such as data preparation, model training, and model evaluation. AWS-Certified-Machine-Learning-Specialty exam covers a wide range of topics, including machine learning algorithms, AWS services such as Amazon SageMaker, and data analysis techniques. Candidates must also demonstrate their ability to design, deploy, and maintain machine learning solutions using AWS services.

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is an excellent opportunity for professionals who want to validate their skills and knowledge in machine learning on the AWS cloud platform. AWS Certified Machine Learning - Specialty certification exam covers a wide range of topics and is designed to test the skills and knowledge of individuals who work with machine learning. By passing the exam, candidates will demonstrate their expertise in machine learning and increase their value as professionals in this field.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q126-Q131):

NEW QUESTION # 126
A machine learning (ML) developer for an online retailer recently uploaded a sales dataset into Amazon SageMaker Studio. The ML developer wants to obtain importance scores for each feature of the dataset. The ML developer will use the importance scores to feature engineer the dataset.
Which solution will meet this requirement with the LEAST development effort?

• A. Use a SageMaker notebook instance to perform a singular value decomposition analysis.
• B. Use SageMaker Data Wrangler to perform a Gini importance score analysis.
• C. Use a SageMaker notebook instance to perform principal component analysis (PCA).
• D. Use the multicollinearity feature to perform a lasso feature selection to perform an importance scores analysis.

Answer: B

Explanation:
SageMaker Data Wrangler is a feature of SageMaker Studio that provides an end-to-end solution for importing, preparing, transforming, featurizing, and analyzing data. Data Wrangler includes built-in analyses that help generate visualizations and data insights in a few clicks. One of the built-in analyses is the Quick Model visualization, which can be used to quickly evaluate the data and produce importance scores for each feature. A feature importance score indicates how useful a feature is at predicting a target label. The feature importance score is between [0, 1] and a higher number indicates that the feature is more important to the whole dataset. The Quick Model visualization uses a random forest model to calculate the feature importance for each feature using the Gini importance method. This method measures the total reduction in node impurity (a measure of how well a node separates the classes) that is attributed to splitting on a particular feature. The ML developer can use the Quick Model visualization to obtain the importance scores for each feature of the dataset and use them to feature engineer the dataset. This solution requires the least development effort compared to the other options.
References:
* Analyze and Visualize
* Detect multicollinearity, target leakage, and feature correlation with Amazon SageMaker Data Wrangler

NEW QUESTION # 127
A large company has developed a B1 application that generates reports and dashboards using data collected from various operational metrics The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports The company wants the executives to be able ask questions using written and spoken interlaces Which combination of services can be used to build this conversational interface? (Select THREE)

• A. Amazon Poly
• B. Amazon Lex
• C. Amazon Transcribe
• D. Amazon Comprehend
• E. Alexa for Business
• F. Amazon Connect

Answer: B,C,D

Explanation:
Explanation
To build a conversational interface that can use natural language to get data from the reports, the company can use a combination of services that can handle both written and spoken inputs, understand the user's intent and query, and extract the relevant information from the reports. The services that can be used for this purpose are:
Amazon Lex: A service for building conversational interfaces into any application using voice and text. Amazon Lex can create chatbots that can interact with users using natural language, and integrate with other AWS services such as Amazon Connect, Amazon Comprehend, and Amazon Transcribe. Amazon Lex can also use lambda functions to implement the business logic and fulfill the user's requests.
Amazon Comprehend: A service for natural language processing and text analytics. Amazon Comprehend can analyze text and speech inputs and extract insights such as entities, key phrases, sentiment, syntax, and topics. Amazon Comprehend can also use custom classifiers and entity recognizers to identify specific terms and concepts that are relevant to the domain of the reports.
Amazon Transcribe: A service for speech-to-text conversion. Amazon Transcribe can transcribe audio inputs into text outputs, and add punctuation and formatting. Amazon Transcribe can also use custom vocabularies and language models to improve the accuracy and quality of the transcription for the specific domain of the reports.
Therefore, the company can use the following architecture to build the conversational interface:
Use Amazon Lex to create a chatbot that can accept both written and spoken inputs from the executives. The chatbot can use intents, utterances, and slots to capture the user's query and parameters, such as the report name, date, metric, or filter.
Use Amazon Transcribe to convert the spoken inputs into text outputs, and pass them to Amazon Lex. Amazon Transcribe can use a custom vocabulary and language model to recognize the terms and concepts related to the reports.
Use Amazon Comprehend to analyze the text inputs and outputs, and extract the relevant information from the reports. Amazon Comprehend can use a custom classifier and entity recognizer to identify the report name, date, metric, or filter from the user's query, and the corresponding data from the reports.
Use a lambda function to implement the business logic and fulfillment of the user's query, such as retrieving the data from the reports, performing calculations or aggregations, and formatting the response. The lambda function can also handle errors and validations, and provide feedback to the user.
Use Amazon Lex to return the response to the user, either in text or speech format, depending on the user's preference.
References:
What Is Amazon Lex?
What Is Amazon Comprehend?
What Is Amazon Transcribe?

NEW QUESTION # 128
A Machine Learning Specialist is developing a custom video recommendation model for an application The dataset used to train this model is very large with millions of data points and is hosted in an Amazon S3 bucket The Specialist wants to avoid loading all of this data onto an Amazon SageMaker notebook instance because it would take hours to move and will exceed the attached 5 GB Amazon EBS volume on the notebook instance.
Which approach allows the Specialist to use all the data to train the model?

• A. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
• B. Use AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
• C. Load a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
• D. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset

Answer: A

Explanation:
Explanation
Pipe input mode is a feature of Amazon SageMaker that allows streaming large datasets from Amazon S3 directly to the training algorithm without downloading them to the local disk. This reduces the startup time, disk space, and cost of training jobs. Pipe input mode is supported by most of the built-in algorithms and can also be used with custom training algorithms. To use Pipe input mode, the data needs to be in a binary format such as protobuf recordIO or TFRecord. The training code needs to use the PipeModeDataset class to read the data from the named pipe provided by SageMaker. To verify that the training code and the model parameters are working as expected, it is recommended to train locally on a smaller subset of the data before launching a full-scale training job on SageMaker. This approach is faster and more efficient than the other options, which involve either downloading the full dataset to an EC2 instance or using AWS Glue, which is not designed for training machine learning models. References:
Using Pipe input mode for Amazon SageMaker algorithms
Using Pipe Mode with Your Own Algorithms
PipeModeDataset Class

NEW QUESTION # 129
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)

• A. AWS Trusted Advisor
• B. AWS Config
• C. AWS Health
• D. AWS CloudTrail
• E. Amazon CloudWatch

Answer: D,E

Explanation:
Explanation
The services that are integrated with Amazon SageMaker to track the information that the Machine Learning Specialist needs are AWS CloudTrail and Amazon CloudWatch. AWS CloudTrail is a service that records the API calls and events for AWS services, including Amazon SageMaker. AWS CloudTrail can track the actions performed by the Data Scientists, such as creating notebooks, training models, and deploying endpoints. AWS CloudTrail can also provide information such as the identity of the user, the time of the action, the parameters used, and the response elements returned. AWS CloudTrail can help the Machine Learning Specialist to monitor the usage and activity of Amazon SageMaker, as well as to audit and troubleshoot any issues1 Amazon CloudWatch is a service that collects and analyzes the metrics and logs for AWS services, including Amazon SageMaker. Amazon CloudWatch can track the performance and utilization of the Amazon SageMaker endpoints, such as the CPU and GPU utilization, the inference latency, the number of invocations, etc. Amazon CloudWatch can also track the errors and alarms that are generated when an endpoint is invoked, such as the model errors, the throttling errors, the HTTP errors, etc. Amazon CloudWatch can help the Machine Learning Specialist to optimize the operational performance and reliability of Amazon SageMaker, as well as to set up notifications and actions based on the metrics and logs

NEW QUESTION # 130
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. 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.
• B. 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.
• 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. 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.

Answer: B

Explanation:
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 # 131
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