AWS Certified Machine Learning - Specialty

• #1

  A Machine Learning Specialist wants to determine the appropriate SageMakerVariantInvocationsPerInstance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5. Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the SageMakerVariantInvocationsPerInstance setting?

  • A10.
  • B30.
  • C600.
  • D2,400.
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• #2

  A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management. Which combination of AWS services will meet these requirements?

  • AAmazon EMR for data discovery, enrichment, and transformation. Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon QuickSight for reporting and getting insights.
  • BAmazon Kinesis Data Analytics for data ingestion. Amazon EMR for data discovery, enrichment, and transformation. Amazon Redshift for querying and analyzing the results in Amazon S3.
  • CAWS Glue for data discovery, enrichment, and transformation. Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon QuickSight for reporting and getting insights.
  • DAWS Data Pipeline for data transfer. AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation. Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL. Amazon QuickSight for reporting and getting insights.
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• #3

  Which characteristic applies to a catalog backup?

  • ACatalog staging files deleted after a successful catalog backup.
  • BA catalog backup can be configured to send disaster recovery information to an e-mail address.
  • CA catalog backup must fit on a single tape.
  • DA catalog backup shuts down the NetBackup database.
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• #4

  A Machine Learning Specialist prepared the following graph displaying the results of K-means for k = [1..10]. Considering the graph, what is a reasonable selection for the optimal choice of k?

  • A1.
  • B4.
  • C7.
  • D10.
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• #5

  A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However, the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC. Why is the ML Specialist not seeing the instance visible in the VPC?

  • AAmazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
  • BAmazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
  • CAmazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  • DAmazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
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• #6

  A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size. What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

  • A1 shards.
  • B10 shards.
  • C100 shards.
  • D1,000 shards.
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• #7

  A Data Science team within a large company uses Amazon SageMaker notebooks to access data stored in Amazon S3 buckets. The IT Security team is concerned that internet-enabled notebook instances create a security vulnerability where malicious code running on the instances could compromise data privacy. The company mandates that all instances stay within a secured VPC with no internet access, and data communication traffic must stay within the AWS network. How should the Data Science team configure the notebook instance placement to meet these requirements?

  • AAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Place the Amazon SageMaker endpoint and S3 buckets within the same VPC.
  • BAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Use IAM policies to grant access to Amazon S3 and Amazon SageMaker.
  • CAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has S3 VPC endpoints and Amazon SageMaker VPC endpoints attached to it.
  • DAssociate the Amazon SageMaker notebook with a private subnet in a VPC. Ensure the VPC has a NAT gateway and an associated security group allowing only outbound connections to Amazon S3 and Amazon SageMaker.
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• #8

  A Machine Learning Specialist previously trained a Logistic Regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only. What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

  • ABuild the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
  • BSerialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
  • CSerialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
  • DBuild the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
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• #9

  A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive. The model produces the following confusion matrix after evaluating on a test dataset of 100 customers. Based on the model evaluation results, why is this a viable model for production?

  • AThe model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
  • BThe precision of the model is 86%, which is less than the accuracy of the model.
  • CThe model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
  • DThe precision of the model is 86%, which is greater than the accuracy of the model.
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• #10

  A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as Linear Regression and Logistic Regression. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable. What should be done to reduce the impact of having such a large number of features?

  • APerform one-hot encoding on highly correlated features.
  • BUse matrix multiplication on highly correlated features.
  • CCreate a new feature space using Principal Component Analysis (PCA).
  • DApply the Pearson correlation coefficient.
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• #11

  A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline. Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?

  • ACreate a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.
  • BCreate an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
  • CCreate a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
  • DCreate a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify. Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.
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• #12

  A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users. Which storage option provides the most processing flexibility and will allow access control with IAM?

  • AUse a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.
  • BUse an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.
  • CSetup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.
  • DConfigure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.
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• #13

  An interactive online dictionary wants to add a widget that displays words used in similar contexts. A Machine Learning Specialist is asked to provide word features for the downstream nearest neighbor model powering the widget. What should the Specialist do to meet these requirements?

  • ACreate one-hot word encoding vectors.
  • BProduce a set of synonyms for every word using Amazon Mechanical Turk.
  • CCreate word embedding vectors that store edit distance with every other word.
  • DDownload word embeddings pre-trained on a large corpus.
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• #14

  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?

  • AUse Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
  • BUse Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
  • CRewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
  • DPrepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.
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• #15

  A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent. How should the Specialist frame this business problem?

  • AStreaming classification.
  • BBinary classification.
  • CMulti-category classification.
  • DRegression classification.
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• #16

  A manufacturer of car engines collects data from cars as they are being driven. The data collected includes timestamp, engine temperature, rotations per minute (RPM), and other sensor readings. The company wants to predict when an engine is going to have a problem, so it can notify drivers in advance to get engine maintenance. The engine data is loaded into a data lake for training. Which is the MOST suitable predictive model that can be deployed into production?

  • AAdd labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a Recurrent Neural Network (RNN) to train the model to recognize when an engine might need maintenance for a certain fault.
  • BThis data requires an unsupervised learning algorithm. Use Amazon SageMaker K-means to cluster the data.
  • CAdd labels over time to indicate which engine faults occur at what time in the future to turn this into a supervised learning problem. Use a Convolutional Neural Network (CNN) to train the model to recognize when an engine might need maintenance for a certain fault.
  • DThis data is already formulated as a time series. Use Amazon SageMaker seq2seq to model the time series.
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• #17

  A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively. How should the Specialist address this issue and what is the reason behind it?

  • AThe learning rate should be increased because the optimization process was trapped at a local minimum.
  • BThe dropout rate at the flatten layer should be increased because the model is not generalized enough.
  • CThe dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
  • DThe epoch number should be increased because the optimization process was terminated before it reached the global minimum.
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• #18

  Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published. A sample of the data being used is below. Given the dataset, the Specialist wants to convert the Day_Of_Week column to binary values. What technique should be used to convert this column to binary values?

  • ABinarization.
  • BOne-hot encoding.
  • CTokenization.
  • DNormalization transformation.
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• #192 चुनें

  A machine learning (ML) specialist wants to secure calls to the Amazon SageMaker Service API. The specialist has configured Amazon VPC with a VPC interface endpoint for the Amazon SageMaker Service API and is attempting to secure traffic from specific sets of instances and IAM users. The VPC is configured with a single public subnet. Which combination of steps should the ML specialist take to secure the traffic? (Choose two.)

  • AAdd a VPC endpoint policy to allow access to the IAM users.
  • BModify the users' IAM policy to allow access to Amazon SageMaker Service API calls only.
  • CModify the security group on the endpoint network interface to restrict access to the instances.
  • DModify the ACL on the endpoint network interface to restrict access to the instances.
  • EAdd a SageMaker Runtime VPC endpoint interface to the VPC.
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• #20

  A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age. Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population. How should the Data Scientist correct this issue?

  • ADrop all records from the dataset where age has been set to 0.
  • BReplace the age field value for records with a value of 0 with the mean or median value from the dataset.
  • CDrop the age feature from the dataset and train the model using the rest of the features.
  • DUse K-means clustering to handle missing features.
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