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The AWS Machine Learning Certification attests to your mastery of developing, altering, training, and deploying Machine Learning (ML) models on AWS. It helps companies locate and develop employees who have the fundamental abilities required to carry out cloud activities.
| Format of Exam | Multiple Choice Questions, Drag and Drop, Multiple Answers, Scenario-based |
| Exam fee: | USD 300$ |
| Exam language: | English, Korean, Japanese, Simplified Chinese |
| Exam Time Duration | 180 minutes |
| Total Questions: | 65 questions |
| Passing score | 750/1000 Or 75% |
AWS CLOUD machine learning advancements and workspaces are created, designed, and delivered by someone with two years of extensive expertise and careful observation.
1.1 Create data repositories for machine learning.
1.2 Identify and implement a data-ingestion solution.
1.3 Identify and implement a data-transformation solution.
2.1 Sanitize and prepare data for modeling.
2.2 Perform feature engineering.
2.3 Analyze and visualize data for machine learning.
3.1 Frame business problems as machine learning problems.
3.2 Select the appropriate model(s) for a given machine learning problem.
3.3 Train machine learning models.
3.4 Perform hyperparameter optimization.
4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
4.2 Recommend and implement the appropriate machine learning services and features for a given problem.
4.3 Apply basic AWS security practices to machine learning solutions.
4.4 Deploy and operationalize machine learning solutions.
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A company is creating an application to identify, count, and classify animal images that areuploaded to the company’s website. The company is using the Amazon SageMaker imageclassification algorithm with an ImageNetV2 convolutional neural network (CNN). Thesolution works well for most animal images but does not recognize many animal speciesthat are less common.The company obtains 10,000 labeled images of less common animal species and storesthe images in Amazon S3. A machine learning (ML) engineer needs to incorporate theimages into the model by using Pipe mode in SageMaker.Which combination of steps should the ML engineer take to train the model? (Choose two.)
A. Use a ResNet model. Initiate full training mode by initializing the network with randomweights.
B. Use an Inception model that is available with the SageMaker image classificationalgorithm.
C. Create a .lst file that contains a list of image files and corresponding class labels. Uploadthe .lst file to Amazon S3.
D. Initiate transfer learning. Train the model by using the images of less common species.
E. Use an augmented manifest file in JSON Lines format.
ANSWER : C,D
A company operates large cranes at a busy port. The company plans to use machinelearning (ML) for predictive maintenance of the cranes to avoid unexpected breakdownsand to improve productivity.The company already uses sensor data from each crane to monitor the health of thecranes in real time. The sensor data includes rotation speed, tension, energy consumption,vibration, pressure, and …perature for each crane. The company contracts AWS MLexperts to implement an ML solution.Which potential findings would indicate that an ML-based solution is suitable for thisscenario? (Select TWO.)
A. The historical sensor data does not include a significant number of data points andattributes for certain time periods.
B. The historical sensor data shows that simple rule-based thresholds can predict cranefailures.
C. The historical sensor data contains failure data for only one type of crane model that isin operation and lacks failure data of most other types of crane that are in operation.
D. The historical sensor data from the cranes are available with high granularity for the last3 years.
E. The historical sensor data contains most common types of crane failures that thecompany wants to predict.
ANSWER : D,E
A company wants to forecast the daily price of newly launched products based on 3 yearsof data for older product prices, sales, and rebates. The time-series data has irregulartimestamps and is missing some values.Data scientist must build a dataset to replace the missing values. The data scientist needsa solution that resamptes the data daily and exports the data for further modeling.Which solution will meet these requirements with the LEAST implementation effort?
A. Use Amazon EMR Serveriess with PySpark.
B. Use AWS Glue DataBrew.
C. Use Amazon SageMaker Studio Data Wrangler.
D. Use Amazon SageMaker Studio Notebook with Pandas.
ANSWER : C
A company wants to predict stock market price trends. The company stores stock marketdata each business day in Amazon S3 in Apache Parquet format. The company stores 20GB of data each day for each stock code.A data engineer must use Apache Spark to perform batch preprocessing datatransformations quickly so the company can complete prediction jobs before the stockmarket opens the next day. The company plans to track more stock market codes andneeds a way to scale the preprocessing data transformations.Which AWS service or feature will meet these requirements with the LEAST developmenteffort over time?
A. AWS Glue jobs
B. Amazon EMR cluster
C. Amazon Athena
D. AWS Lambda
ANSWER : A
An ecommerce company wants to use machine learning (ML) to monitor fraudulenttransactions on its website. The company is using Amazon SageMaker to research, train,deploy, and monitor the ML models.The historical transactions data is in a .csv file that is stored in Amazon S3 The datacontains features such as the user's IP address, navigation time, average time on eachpage, and the number of clicks for ....session. There is no label in the data to indicate if atransaction is anomalous.Which models should the company use in combination to detect anomalous transactions?(Select TWO.)
A. IP Insights
B. K-nearest neighbors (k-NN)
C. Linear learner with a logistic function
D. Random Cut Forest (RCF)
E. XGBoost
ANSWER : D,E
A data scientist is working on a public sector project for an urban traffic system. Whilestudying the traffic patterns, it is clear to the data scientist that the traffic behavior at eachlight is correlated, subject to a small stochastic error term. The data scientist must modelthe traffic behavior to analyze the traffic patterns and reduce congestion.How will the data scientist MOST effectively model the problem?
A. The data scientist should obtain a correlated equilibrium policy by formulating thisproblem as a multi-agent reinforcement learning problem.
B. The data scientist should obtain the optimal equilibrium policy by formulating thisproblem as a single-agent reinforcement learning problem.
C. Rather than finding an equilibrium policy, the data scientist should obtain accuratepredictors of traffic flow by using historical data through a supervised learning approach.
D. Rather than finding an equilibrium policy, the data scientist should obtain accuratepredictors of traffic flow by using unlabeled simulated data representing the new trafficpatterns in the city and applying an unsupervised learning approach.
ANSWER : A
A company wants to create an artificial intelligence (Al) yoga instructor that can lead largeclasses of students. The company needs to create a feature that can accurately count thenumber of students who are in a class. The company also needs a feature that candifferentiate students who are performing a yoga stretch correctly from students who areperforming a stretch incorrectly....etermine whether students are performing a stretch correctly, the solution needs tomeasure the location and angle of each student's arms and legs A data scientist must useAmazon SageMaker to ...ss video footage of a yoga class by extracting image frames andapplying computer vision models.Which combination of models will meet these requirements with the LEAST effort? (SelectTWO.)
A. Image Classification
B. Optical Character Recognition (OCR)
C. Object Detection
D. Pose estimation
E. Image Generative Adversarial Networks (GANs)
ANSWER : C,D
A company wants to predict the classification of documents that are created from anapplication. New documents are saved to an Amazon S3 bucket every 3 seconds. Thecompany has developed three versions of a machine learning (ML) model within AmazonSageMaker to classify document text. The company wants to deploy these three versions to predict the classification of each document.Which approach will meet these requirements with the LEAST operational overhead?
A. Configure an S3 event notification that invokes an AWS Lambda function when newdocuments are created. Configure the Lambda function to create three SageMaker batchtransform jobs, one batch transform job for each model for each document.
B. Deploy all the models to a single SageMaker endpoint. Treat each model as aproduction variant. Configure an S3 event notification that invokes an AWS Lambdafunction when new documents are created. Configure the Lambda function to call eachproduction variant and return the results of each model.
C. Deploy each model to its own SageMaker endpoint Configure an S3 event notificationthat invokes an AWS Lambda function when new documents are created. Configure theLambda function to call each endpoint and return the results of each model.
D. Deploy each model to its own SageMaker endpoint. Create three AWS Lambdafunctions. Configure each Lambda function to call a different endpoint and return theresults. Configure three S3 event notifications to invoke the Lambda functions when newdocuments are created.
ANSWER : B
A company is using Amazon Polly to translate plaintext documents to speech forautomated company announcements However company acronyms are beingmispronounced in the current documents How should a Machine Learning Specialistaddress this issue for future documents?
A. Convert current documents to SSML with pronunciation tags
B. Create an appropriate pronunciation lexicon.
C. Output speech marks to guide in pronunciation
D. Use Amazon Lex to preprocess the text files for pronunciation
ANSWER : B
An online delivery company wants to choose the fastest courier for each delivery at themoment an order is placed. The company wants to implement this feature for existing usersand new users of its application. Data scientists have trained separate models withXGBoost for this purpose, and the models are stored in Amazon S3. There is one model fofeach city where the company operates.The engineers are hosting these models in Amazon EC2 for responding to the web clientrequests, with one instance for each model, but the instances have only a 5% utilization inCPU and memory, ....operation engineers want to avoid managing unnecessary resources.Which solution will enable the company to achieve its goal with the LEAST operationaloverhead?
A. Create an Amazon SageMaker notebook instance for pulling all the models fromAmazon S3 using the boto3 library. Remove the existing instances and use the notebook toperform a SageMaker batch transform for performing inferences offline for all the possibleusers in all the cities. Store the results in different files in Amazon S3. Point the web clientto the files.
B. Prepare an Amazon SageMaker Docker container based on the open-source multimodelserver. Remove the existing instances and create a multi-model endpoint inSageMaker instead, pointing to the S3 bucket containing all the models Invoke theendpoint from the web client at runtime, specifying the TargetModel parameter according tothe city of each request.
C. Keep only a single EC2 instance for hosting all the models. Install a model server in theinstance and load each model by pulling it from Amazon S3. Integrate the instance with theweb client using Amazon API Gateway for responding to the requests in real time,specifying the target resource according to the city of each request.
D. Prepare a Docker container based on the prebuilt images in Amazon SageMaker.Replace the existing instances with separate SageMaker endpoints. one for each citywhere the company operates. Invoke the endpoints from the web client, specifying the URL and EndpomtName parameter according to the city of each request.
ANSWER : B
A data engineer is preparing a dataset that a retail company will use to predict the numberof visitors to stores. The data engineer created an Amazon S3 bucket. The engineersubscribed the S3 bucket to an AWS Data Exchange data product for general economicindicators. The data engineer wants to join the economic indicator data to an existing tablein Amazon Athena to merge with the business data. All these transformations must finishrunning in 30-60 minutes.Which solution will meet these requirements MOST cost-effectively?
A. Configure the AWS Data Exchange product as a producer for an Amazon Kinesis datastream. Use an Amazon Kinesis Data Firehose delivery stream to transfer the data toAmazon S3 Run an AWS Glue job that will merge the existing business data with theAthena table. Write the result set back to Amazon S3.
B. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS Lambdafunction. Program the Lambda function to use Amazon SageMaker Data Wrangler tomerge the existing business data with the Athena table. Write the result set back toAmazon S3.
C. Use an S3 event on the AWS Data Exchange S3 bucket to invoke an AWS LambdaFunction Program the Lambda function to run an AWS Glue job that will merge the existingbusiness data with the Athena table Write the results back to Amazon S3.
D. Provision an Amazon Redshift cluster. Subscribe to the AWS Data Exchange productand use the product to create an Amazon Redshift Table Merge the data in AmazonRedshift. Write the results back to Amazon S3.
ANSWER : B
A machine learning (ML) specialist is using Amazon SageMaker hyperparameteroptimization (HPO) to improve a model’s accuracy. The learning rate parameter is specifiedin the following HPO configuration: During the results analysis, the ML specialist determines that most of the training jobs hada learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01.Training jobs need to run regularly over a changing dataset. The ML specialist needs tofind a tuning mechanism that uses different learning rates more evenly from the providedrange between MinValue and MaxValue.Which solution provides the MOST accurate result?
A.Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this HPO job.
B.Run three different HPO jobs that use different learning rates form the following intervalsfor MinValue and MaxValue while using the same number of training jobs for each HPOjob:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.
C.Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this training job.
D.Run three different HPO jobs that use different learning rates form the following intervalsfor MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:[0.01, 0.1][0.001, 0.01][0.0001, 0.001]Select the most accurate hyperparameter configuration form these three HPO jobs.
ANSWER : C
A retail company wants to build a recommendation system for the company's website. Thesystem needs to provide recommendations for existing users and needs to base thoserecommendations on each user's past browsing history. The system also must filter out anyitems that the user previously purchased.Which solution will meet these requirements with the LEAST development effort?
A. Train a model by using a user-based collaborative filtering algorithm on AmazonSageMaker. Host the model on a SageMaker real-time endpoint. Configure an Amazon APIGateway API and an AWS Lambda function to handle real-time inference requests that theweb application sends. Exclude the items that the user previously purchased from theresults before sending the results back to the web application.
B. Use an Amazon Personalize PERSONALIZED_RANKING recipe to train a model.Create a real-time filter to exclude items that the user previously purchased. Create anddeploy a campaign on Amazon Personalize. Use the GetPersonalizedRanking APIoperation to get the real-time recommendations.
C. Use an Amazon Personalize USER_ PERSONAL IZATION recipe to train a modelCreate a real-time filter to exclude items that the user previously purchased. Create anddeploy a campaign on Amazon Personalize. Use the GetRecommendations API operationto get the real-time recommendations.
D. Train a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host the model on a SageMaker real-time endpoint. Configure an Amazon APIGateway API and an AWS Lambda function to handle real-time inference requests that theweb application sends. Exclude the items that the user previously purchased from theresults before sending the results back to the web application.
ANSWER : C
A company deployed a machine learning (ML) model on the company website to predictreal estate prices. Several months after deployment, an ML engineer notices that theaccuracy of the model has gradually decreased.The ML engineer needs to improve the accuracy of the model. The engineer also needs toreceive notifications for any future performance issues.Which solution will meet these requirements?
A. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
B. Use Amazon SageMaker Model Governance. Configure Model Governance toautomatically adjust model hyper para meters. Create a performance threshold alarm inAmazon CloudWatch to send notifications.
C. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger tosend Amazon CloudWatch alarms to alert the team Retrain the model by using only datafrom the previous several months.
D. Use only data from the previous several months to perform incremental training toupdate the model. Use Amazon SageMaker Model Monitor to detect model performanceissues and to send notifications.
ANSWER : A
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