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Amazon MLS-C01 Sample Questions
Question # 1
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. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity. B. Include a policy statement for the data scientist's 1AM user that allows the 1AM user toperform the sagemaker: lnvokeEndpoint action, C. Include an inline policy for the data scientist’s 1AM user that allows SageMaker to readS3 objects D. Include a policy statement for the data scientist's 1AM user that allows the 1AM user toperform the sagemakerGetRecord action. E. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in theAthena SQL query. F. Perform a user remapping in SageMaker to map the 1AM user to another 1AM user thatis on the hosted endpoint.
Answer: B,C,E 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 hasthe necessary permissions, access, and syntax to query the ML model from Athena. Theseactions have the following benefits:B: Including a policy statement for the IAM user that allows thesagemaker:InvokeEndpoint action grants the IAM user the permission to call theSageMaker Runtime InvokeEndpoint API, which is used to get inferences from themodel hosted at the endpoint1.C: Including an inline policy for the IAM user that allows SageMaker to read S3objects enables the IAM user to access the data stored in S3, which is the sourceof the Athena queries2.E: Including the SQL statement “USING EXTERNAL FUNCTIONml_function_name” in the Athena SQL query allows the IAM user to invoke the MLmodel as an external function from Athena, which is a feature that enablesquerying ML models from SQL statements3.The other options are not correct or necessary, because they have the followingdrawbacks:A: Attaching the AmazonAthenaFullAccess AWS managed policy to the useridentity is not sufficient, because it does not grant the IAM user the permission toinvoke 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 toperform the sagemaker:GetRecord action is not relevant, because this action isused to retrieve a single record from a feature group, which is not the case in thisscenario5.F: Performing a user remapping in SageMaker to map the IAM user to anotherIAM user that is on the hosted endpoint is not applicable, because this feature isonly available for multi-model endpoints, which are not used in this scenario.References:1: InvokeEndpoint - Amazon SageMaker2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena3: Querying machine learning models from Amazon Athena using AmazonSageMaker | AWS Machine Learning Blog 4: AmazonAthenaFullAccess - AWS Identity and Access Management5: GetRecord - Amazon SageMaker Feature Store Runtime: [Invoke a Multi-Model Endpoint - Amazon SageMaker]
Question # 2
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords. Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?
A. Use Amazon SageMaker script mode and use train.py unchanged. Point the AmazonSageMaker training invocation to the local path of the data without reformatting the trainingdata. B. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecorddata into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3bucket without reformatting the training data. C. Rewrite the train.py script to add a section that converts TFRecords to protobuf andingests the protobuf data instead of TFRecords. D. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue orAWS Lambda to reformat and store the data in an Amazon S3 bucket.
Answer: B Explanation: Amazon SageMaker script mode is a feature that allows users to use training scripts similar to those they would use outside SageMaker with SageMaker’s prebuiltcontainers for various frameworks such as TensorFlow. Script mode supports reading datafrom Amazon S3 buckets without requiring any changes to the training script. Therefore,option B is the best method of providing training data to Amazon SageMaker that wouldmeet the business requirements with the least development overhead.Option A is incorrect because using a local path of the data would not be scalable orreliable, as it would depend on the availability and capacity of the local storage. Moreover,using a local path of the data would not leverage the benefits of Amazon S3, such asdurability, security, and performance. Option C is incorrect because rewriting the train.pyscript to convert TFRecords to protobuf would require additional development effort andcomplexity, as well as introduce potential errors and inconsistencies in the data format.Option D is incorrect because preparing the data in the format accepted by AmazonSageMaker would also require additional development effort and complexity, as well asinvolve using additional services such as AWS Glue or AWS Lambda, which wouldincrease the cost and maintenance of the solution.References:Bring your own model with Amazon SageMaker script modeGitHub - aws-samples/amazon-sagemaker-script-modeDeep Dive on TensorFlow training with Amazon SageMaker and Amazon S3amazon-sagemaker-script-mode/generate_cifar10_tfrecords.py at master
Question # 3
A credit card company wants to identify fraudulent transactions in real time. A data scientist builds a machine learning model for this purpose. The transactional data is captured and stored in Amazon S3. The historic data is already labeled with two classes: fraud (positive) and fair transactions (negative). The data scientist removes all the missing data and builds a classifier by using the XGBoost algorithm in Amazon SageMaker. The model produces the following results: • True positive rate (TPR): 0.700 • False negative rate (FNR): 0.300 • True negative rate (TNR): 0.977 • False positive rate (FPR): 0.023 • Overall accuracy: 0.949 Which solution should the data scientist use to improve the performance of the model?
A. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the minority class inthe training dataset. Retrain the model with the updated training data. B. Apply the Synthetic Minority Oversampling Technique (SMOTE) on the majority class in the training dataset. Retrain the model with the updated training data. C. Undersample the minority class. D. Oversample the majority class.
Answer: A Explanation: The solution that the data scientist should use to improve the performance of the model is to apply the Synthetic Minority Oversampling Technique (SMOTE) on theminority class in the training dataset, and retrain the model with the updated training data.This solution can address the problem of class imbalance in the dataset, which can affectthe model’s ability to learn from the rare but important positive class (fraud).Class imbalance is a common issue in machine learning, especially for classification tasks.It occurs when one class (usually the positive or target class) is significantlyunderrepresented in the dataset compared to the other class (usually the negative or nontargetclass). For example, in the credit card fraud detection problem, the positive class(fraud) is much less frequent than the negative class (fair transactions). This can cause themodel to be biased towards the majority class, and fail to capture the characteristics andpatterns of the minority class. As a result, the model may have a high overall accuracy, buta low recall or true positive rate for the minority class, which means it misses manyfraudulent transactions.SMOTE is a technique that can help mitigate the class imbalance problem by generatingsynthetic samples for the minority class. SMOTE works by finding the k-nearest neighborsof each minority class instance, and randomly creating new instances along the linesegments connecting them. This way, SMOTE can increase the number and diversity ofthe minority class instances, without duplicating or losing any information. By applyingSMOTE on the minority class in the training dataset, the data scientist can balance theclasses and improve the model’s performance on the positive class1.The other options are either ineffective or counterproductive. Applying SMOTE on themajority class would not balance the classes, but increase the imbalance and the size ofthe dataset. Undersampling the minority class would reduce the number of instancesavailable for the model to learn from, and potentially lose some important information.Oversampling the majority class would also increase the imbalance and the size of thedataset, and introduce redundancy and overfitting.References:1: SMOTE for Imbalanced Classification with Python - Machine Learning Mastery
Question # 4
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings. The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority. A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic. Which algorithms are best suited to this scenario? (Choose two.)
A. Latent Dirichlet allocation (LDA) B. Random Forest classifier C. Neural topic modeling (NTM) D. Linear support vector machine E. Linear regression
Answer: A,C Explanation: The algorithms that are best suited to this scenario are latent Dirichletallocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learningmethods that can discover abstract topics from a collection of text documents. LDA andNTM can provide a list of the top words for each topic, as well as the topic distribution foreach document, which can help the auditors assess the relevance and priority of thetopic12.The other options are not suitable because:Option B: A random forest classifier is a supervised learning method that canperform classification or regression tasks by using an ensemble of decisiontrees. A random forest classifier is not suitable for discovering abstract topics fromtext documents, as it requires labeled data and predefined classes3.Option D: A linear support vector machine is a supervised learning method thatcan perform classification or regression tasks by using a linear function thatseparates the data into different classes. A linear support vector machine is notsuitable for discovering abstract topics from text documents, as it requires labeleddata and predefined classes4.Option E: A linear regression is a supervised learning method that can performregression tasks by using a linear function that models the relationship between adependent variable and one or more independent variables. A linear regression isnot suitable for discovering abstract topics from text documents, as it requireslabeled data and a continuous output variable5.References:1: Latent Dirichlet Allocation2: Neural Topic Modeling3: Random Forest Classifier4: Linear Support Vector Machine5: Linear Regression
Question # 5
A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations. Which solution will meet these requirements with LEAST development effort?
A. Use AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail tocapture IP address and timestamp details. B. Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWSPanorama Device SDK to capture IP address and timestamp details. C. Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail tocapture IP address and timestamp details. D. Use Amazon Rekognition to identify celebrities in the pictures. Use the text detectionfeature to capture IP address and timestamp details.
Answer: C Explanation: The solution C will meet the requirements with the least development effortbecause it uses Amazon Rekognition and AWS CloudTrail, which are fully managedservices that can provide the desired functionality. The solution C involves the followingsteps:Use Amazon Rekognition to identify celebrities in the pictures. AmazonRekognition is a service that can analyze images and videos and extract insightssuch as faces, objects, scenes, emotions, and more. Amazon Rekognition alsoprovides a feature called Celebrity Recognition, which can recognize thousands ofcelebrities across a number of categories, such as politics, sports, entertainment,and media. Amazon Rekognition can return the name, face, and confidence scoreof the recognized celebrities, as well as additional information such as URLs andbiographies1.Use AWS CloudTrail to capture IP address and timestamp details. AWS CloudTrailis a service that can record the API calls and events made by or on behalf of AWSaccounts. AWS CloudTrail can provide information such as the source IP address,the user identity, the request parameters, and the response elements of the APIcalls. AWS CloudTrail can also deliver the event records to an Amazon S3 bucketor an Amazon CloudWatch Logs group for further analysis and auditing2.The other options are not suitable because:Option A: Using AWS Panorama to identify celebrities in the pictures and usingAWS CloudTrail to capture IP address and timestamp details will not meet therequirements effectively. AWS Panorama is a service that can extend computervision to the edge, where it can run inference on video streams from cameras andother devices. AWS Panorama is not designed for identifying celebrities inpictures, and it may not provide accurate or relevant results. Moreover, AWSPanorama requires the use of an AWS Panorama Appliance or a compatibledevice, which may incur additional costs and complexity3.Option B: Using AWS Panorama to identify celebrities in the pictures and makingcalls to the AWS Panorama Device SDK to capture IP address and timestampdetails will not meet the requirements effectively, for the same reasons as optionA. Additionally, making calls to the AWS Panorama Device SDK will require moredevelopment effort than using AWS CloudTrail, as it will involve writing customcode and handling errors and exceptions4.Option D: Using Amazon Rekognition to identify celebrities in the pictures andusing the text detection feature to capture IP address and timestamp details willnot meet the requirements effectively. The text detection feature of AmazonRekognition is used to detect and recognize text in images and videos, such asstreet names, captions, product names, and license plates. It is not suitable forcapturing IP address and timestamp details, as these are not part of the picturesthat users upload. Moreover, the text detection feature may not be accurate orreliable, as it depends on the quality and clarity of the text in the images andvideos5.References:1: Amazon Rekognition Celebrity Recognition 2: AWS CloudTrail Overview3: AWS Panorama Overview4: AWS Panorama Device SDK5: Amazon Rekognition Text Detection
Question # 6
A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional data. The company also wants to perform transformations on the transactional data that is in Amazon S3. The company wants to use a machine learning (ML) approach to detect fraud in the transformed data. Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)
A. Use Amazon Athena to scan the data and identify the schema. B. Use AWS Glue crawlers to scan the data and identify the schema. C. Use Amazon Redshift to store procedures to perform data transformations D. Use AWS Glue workflows and AWS Glue jobs to perform data transformations. E. Use Amazon Redshift ML to train a model to detect fraud. F. Use Amazon Fraud Detector to train a model to detect fraud.
Answer: B,D,F Explanation: To meet the requirements with the least operational overhead, the company should use AWS Glue crawlers, AWS Glue workflows and jobs, and Amazon FraudDetector. AWS Glue crawlers can scan the data in Amazon S3 and identify the schema,which is then stored in the AWS Glue Data Catalog. AWS Glue workflows and jobs canperform data transformations on the data in Amazon S3 using serverless Spark or Pythonscripts. Amazon Fraud Detector can train a model to detect fraud using the transformeddata and the company’s historical fraud labels, and then generate fraud predictions using asimple API call.Option A is incorrect because Amazon Athena is a serverless query service that cananalyze data in Amazon S3 using standard SQL, but it does not perform datatransformations or fraud detection.Option C is incorrect because Amazon Redshift is a cloud data warehouse that can storeand query data using SQL, but it requires provisioning and managing clusters, which addsoperational overhead. Moreover, Amazon Redshift does not provide a built-in fraud detection capability.Option E is incorrect because Amazon Redshift ML is a feature that allows users to create,train, and deploy machine learning models using SQL commands in Amazon Redshift.However, using Amazon Redshift ML would require loading the data from Amazon S3 toAmazon Redshift, which adds complexity and cost. Also, Amazon Redshift ML does notsupport fraud detection as a use case.References:AWS Glue CrawlersAWS Glue Workflows and JobsAmazon Fraud Detector
Question # 7
An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK. The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy. Which solution will improve the computational efficiency of the models?
A. Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights,gradients, biases, and activation outputs. Compute the filter ranks based on the traininginformation. Apply pruning to remove the low-ranking filters. Set new weights based on thepruned set of filters. Run a new training job with the pruned model. B. Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collecta larger labeled dataset with the labelling workflows. Run a new training job that uses thenew labeled data with previous training data. C. Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients,biases, and activation outputs. Compute the filter ranks based on the training information.Apply pruning to remove the low-ranking filters. Set the new weights based on the prunedset of filters. Run a new training job with the pruned model. D. Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metricand OverheadLatency metric of the model after the company deploys the model. Increasethe model learning rate. Run a new training job.
Answer: C Explanation: The solution C will improve the computational efficiency of the models because it uses Amazon SageMaker Debugger and pruning, which are techniques that canreduce the size and complexity of the convolutional neural network (CNN) models. Thesolution C involves the following steps:Use Amazon SageMaker Debugger to gain visibility into the training weights,gradients, biases, and activation outputs. Amazon SageMaker Debugger is aservice that can capture and analyze the tensors that are emitted during thetraining process of machine learning models. Amazon SageMaker Debugger canprovide insights into the model performance, quality, and convergence. AmazonSageMaker Debugger can also help to identify and diagnose issues such asoverfitting, underfitting, vanishing gradients, and exploding gradients1.Compute the filter ranks based on the training information. Filter ranking is atechnique that can measure the importance of each filter in a convolutional layerbased on some criterion, such as the average percentage of zero activations orthe L1-norm of the filter weights. Filter ranking can help to identify the filters thathave little or no contribution to the model output, and thus can be removed withoutaffecting the model accuracy2.Apply pruning to remove the low-ranking filters. Pruning is a technique that canreduce the size and complexity of a neural network by removing the redundant orirrelevant parts of the network, such as neurons, connections, or filters. Pruningcan help to improve the computational efficiency, memory usage, and inference speed of the model, as well as to prevent overfitting and improve generalization3.Set the new weights based on the pruned set of filters. After pruning, the modelwill have a smaller and simpler architecture, with fewer filters in each convolutionallayer. The new weights of the model can be set based on the pruned set of filters,either by initializing them randomly or by fine-tuning them from the originalweights4.Run a new training job with the pruned model. The pruned model can be trainedagain with the same or a different dataset, using the same or a different frameworkor algorithm. The new training job can use the same or a different configuration ofAmazon SageMaker, such as the instance type, the hyperparameters, or the dataingestion mode. The new training job can also use Amazon SageMaker Debuggerto monitor and analyze the training process and the model quality5.The other options are not suitable because:Option A: Using Amazon CloudWatch metrics to gain visibility into the SageMakertraining weights, gradients, biases, and activation outputs will not be as effectiveas using Amazon SageMaker Debugger. Amazon CloudWatch is a service thatcan monitor and observe the operational health and performance of AWSresources and applications. Amazon CloudWatch can provide metrics, alarms,dashboards, and logs for various AWS services, including Amazon SageMaker.However, Amazon CloudWatch does not provide the same level of granularity anddetail as Amazon SageMaker Debugger for the tensors that are emitted during thetraining process of machine learning models. Amazon CloudWatch metrics aremainly focused on the resource utilization and the training progress, not on themodel performance, quality, and convergence6.Option B: Using Amazon SageMaker Ground Truth to build and run data labelingworkflows and collecting a larger labeled dataset with the labeling workflows willnot improve the computational efficiency of the models. Amazon SageMakerGround Truth is a service that can create high-quality training datasets for machinelearning by using human labelers. A larger labeled dataset can help to improve themodel accuracy and generalization, but it will not reduce the memory, battery, andhardware consumption of the model. Moreover, a larger labeled dataset mayincrease the training time and cost of the model7.Option D: Using Amazon SageMaker Model Monitor to gain visibility into theModelLatency metric and OverheadLatency metric of the model after the companydeploys the model and increasing the model learning rate will not improve thecomputational efficiency of the models. Amazon SageMaker Model Monitor is aservice that can monitor and analyze the quality and performance of machinelearning models that are deployed on Amazon SageMaker endpoints. TheModelLatency metric and the OverheadLatency metric can measure the inferencelatency of the model and the endpoint, respectively. However, these metrics do notprovide any information about the training weights, gradients, biases, andactivation outputs of the model, which are needed for pruning. Moreover,increasing the model learning rate will not reduce the size and complexity of themodel, but it may affect the model convergence and accuracy.References:1: Amazon SageMaker Debugger2: Pruning Convolutional Neural Networks for Resource Efficient Inference3: Pruning Neural Networks: A Survey4: Learning both Weights and Connections for Efficient Neural Networks 5: Amazon SageMaker Training Jobs6: Amazon CloudWatch Metrics for Amazon SageMaker7: Amazon SageMaker Ground Truth: Amazon SageMaker Model Monitor
Question # 8
A media company is building a computer vision model to analyze images that are on social media. The model consists of CNNs that the company trained by using images that the company stores in Amazon S3. The company used an Amazon SageMaker training job in File mode with a single Amazon EC2 On-Demand Instance. Every day, the company updates the model by using about 10,000 images that the company has collected in the last 24 hours. The company configures training with only one epoch. The company wants to speed up training and lower costs without the need to make any code changes. Which solution will meet these requirements?
A. Instead of File mode, configure the SageMaker training job to use Pipe mode. Ingest thedata from a pipe. B. Instead Of File mode, configure the SageMaker training job to use FastFile mode withno Other changes. C. Instead Of On-Demand Instances, configure the SageMaker training job to use SpotInstances. Make no Other changes. D. Instead Of On-Demand Instances, configure the SageMaker training job to use SpotInstances. Implement model checkpoints.
Answer: C Explanation: The solution C will meet the requirements because it uses Amazon SageMaker Spot Instances, which are unused EC2 instances that are available at up to90% discount compared to On-Demand prices. Amazon SageMaker Spot Instances canspeed up training and lower costs by taking advantage of the spare EC2 capacity. Thecompany does not need to make any code changes to use Spot Instances, as it can simplyenable the managed spot training option in the SageMaker training job configuration. Thecompany also does not need to implement model checkpoints, as it is using only oneepoch for training, which means the model will not resume from a previous state1.The other options are not suitable because:Option A: Configuring the SageMaker training job to use Pipe mode instead of Filemode will not speed up training or lower costs significantly. Pipe mode is a dataingestion mode that streams data directly from S3 to the training algorithm, withoutcopying the data to the local storage of the training instance. Pipe mode canreduce the startup time of the training job and the disk space usage, but it does notaffect the computation time or the instance price. Moreover, Pipe mode mayrequire some code changes to handle the streaming data, depending on thetraining algorithm2. Option B: Configuring the SageMaker training job to use FastFile mode instead ofFile mode will not speed up training or lower costs significantly. FastFile mode is adata ingestion mode that copies data from S3 to the local storage of the traininginstance in parallel with the training process. FastFile mode can reduce the startuptime of the training job and the disk space usage, but it does not affect thecomputation time or the instance price. Moreover, FastFile mode is only availablefor distributed training jobs that use multiple instances, which is not the case forthe company3.Option D: Configuring the SageMaker training job to use Spot Instances andimplementing model checkpoints will not meet the requirements without the needto make any code changes. Model checkpoints are a feature that allows thetraining job to save the model state periodically to S3, and resume from the latestcheckpoint if the training job is interrupted. Model checkpoints can help to avoidlosing the training progress and ensure the model convergence, but they requiresome code changes to implement the checkpointing logic and the resuming logic4.References:1: Managed Spot Training - Amazon SageMaker2: Pipe Mode - Amazon SageMaker3: FastFile Mode - Amazon SageMaker4: Checkpoints - Amazon SageMaker
Question # 9
A data scientist is building a forecasting model for a retail company by using the most recent 5 years of sales records that are stored in a data warehouse. The dataset contains sales records for each of the company's stores across five commercial regions The data scientist creates a working dataset with StorelD. Region. Date, and Sales Amount as columns. The data scientist wants to analyze yearly average sales for each region. The scientist also wants to compare how each region performed compared to average sales across all commercial regions. Which visualization will help the data scientist better understand the data trend?
A. Create an aggregated dataset by using the Pandas GroupBy function to get averagesales for each year for each store. Create a bar plot, faceted by year, of average sales foreach store. Add an extra bar in each facet to represent average sales. B. Create an aggregated dataset by using the Pandas GroupBy function to get averagesales for each year for each store. Create a bar plot, colored by region and faceted by year,of average sales for each store. Add a horizontal line in each facet to represent averagesales. C. Create an aggregated dataset by using the Pandas GroupBy function to get averagesales for each year for each region Create a bar plot of average sales for each region. Addan extra bar in each facet to represent average sales. D. Create an aggregated dataset by using the Pandas GroupBy function to get average sales for each year for each region Create a bar plot, faceted by year, of average sales foreach region Add a horizontal line in each facet to represent average sales.
Answer: D Explanation: The best visualization for this task is to create a bar plot, faceted by year, ofaverage sales for each region and add a horizontal line in each facet to represent averagesales. This way, the data scientist can easily compare the yearly average sales for eachregion with the overall average sales and see the trends over time. The bar plot also allowsthe data scientist to see the relative performance of each region within each year andacross years. The other options are less effective because they either do not show theyearly trends, do not show the overall average sales, or do not group the data by region.References:pandas.DataFrame.groupby — pandas 2.1.4 documentationpandas.DataFrame.plot.bar — pandas 2.1.4 documentationMatplotlib - Bar Plot - Online Tutorials Library
Question # 10
A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal. What should the data scientist do to identify and address training issues with the LEAST development effort?
A. Use CPU utilization metrics that are captured in Amazon CloudWatch. Configure aCloudWatch alarm to stop the training job early if low CPU utilization occurs. B. Use high-resolution custom metrics that are captured in Amazon CloudWatch. Configurean AWS Lambda function to analyze the metrics and to stop the training job early if issuesare detected. C. Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rulesto detect issues and to launch the StopTrainingJob action if issues are detected. D. Use the SageMaker Debugger confusion and feature_importance_overweight built-inrules to detect issues and to launch the StopTrainingJob action if issues are detected.
Answer: C Explanation: The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-inrules to detect issues. SageMaker Debugger is a feature of Amazon SageMakerthat allows data scientists to monitor, analyze, and debug machine learningmodels during training. SageMaker Debugger provides a set of built-in rules thatcan automatically detect common issues and anomalies in model training, such asvanishing or exploding gradients, overfitting, underfitting, low GPU utilization, andmore1. The data scientist can use the vanishing_gradient rule to check if thegradients are becoming too small and causing the training to not converge. Thedata scientist can also use the LowGPUUtilization rule to check if the GPUresources are underutilized and causing the training to be inefficient2.Launch the StopTrainingJob action if issues are detected. SageMaker Debuggercan also take actions based on the status of the rules. One of the actions isStopTrainingJob, which can terminate the training job if a rule is in an errorstate. This can help the data scientist to save time and money by stopping thetraining early if issues are detected3.The other options are not suitable because:Option A: Using CPU utilization metrics that are captured in Amazon CloudWatchand configuring a CloudWatch alarm to stop the training job early if low CPUutilization occurs will not identify and address training issues effectively. CPUutilization is not a good indicator of model training performance, especially for GPUinstances. Moreover, CloudWatch alarms can only trigger actions based on simplethresholds, not complex rules or conditions4.Option B: Using high-resolution custom metrics that are captured in AmazonCloudWatch and configuring an AWS Lambda function to analyze the metrics andto stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write thecode for capturing, sending, and analyzing the custom metrics, as well as forinvoking the Lambda function and stopping the training job. Moreover, this solutionmay not be able to detect all the issues that SageMaker Debugger can5.Option D: Using the SageMaker Debugger confusion andfeature_importance_overweight built-in rules and launching the StopTrainingJobaction if issues are detected will not identify and address training issues effectively.The confusion rule is used to monitor the confusion matrix of a classificationmodel, which is not relevant for a regression model that predicts prices. Thefeature_importance_overweight rule is used to check if some features have toomuch weight in the model, which may not be related to the convergence orresource utilization issues2.References:1: Amazon SageMaker Debugger2: Built-in Rules for Amazon SageMaker Debugger3: Actions for Amazon SageMaker Debugger4: Amazon CloudWatch Alarms5: Amazon CloudWatch Custom Metrics
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