AWS Certified Machine Learning - Specialty

• #101

  An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time Which solution should the agency consider?

  • AUse a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
  • BUse a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.
  • CInstall AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non-employees are detected.
  • DInstall AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.
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• #102

  A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers. The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset. Which solution for text extraction and entity detection will require the LEAST amount of effort?

  • AExtract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
  • BExtract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.
  • CExtract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
  • DExtract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
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• #103

  A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of 1,000 hand-labeled images covering 10 distinct items. The training results were poor. Which machine learning approach fulfills the company's long-term needs?

  • AConvert the images to grayscale and retrain the model.
  • BReduce the number of distinct items from 10 to 2, build the model, and iterate.
  • CAttach different colored labels to each item, take the images again, and build the model.
  • DAugment training data for each item using image variants like inversions and translations, build the model, and iterate.
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• #104

  A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier. Total number of images available = 1,000 Test set images = 100 (constant test set). The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners. Which techniques can be used by the ML Specialist to improve this specific test error?

  • AIncrease the training data by adding variation in rotation for training images.
  • BIncrease the number of epochs for model training.
  • CIncrease the number of layers for the neural network.
  • DIncrease the dropout rate for the second-to-last layer.
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• #105

  A credit card company wants to build a credit scoring model to help predict whether a new credit card applicant will default on a credit card payment. The company has collected data from a large number of sources with thousands of raw attributes. Early experiments to train a classification model revealed that many attributes are highly correlated, the large number of features slows down the training speed significantly, and that there are some overfitting issues. The Data Scientist on this project would like to speed up the model training time without losing a lot of information from the original dataset. Which feature engineering technique should the Data Scientist use to meet the objectives?

  • ARun self-correlation on all features and remove highly correlated features.
  • BNormalize all numerical values to be between 0 and 1.
  • CUse an autoencoder or Principal Component Analysis (PCA) to replace original features with new features.
  • DCluster raw data using K-means and use sample data from each cluster to build a new dataset.
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• #106

  A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible. What approach should the Specialist take to accomplish these tasks?

  • AEmbed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.
  • BRun K-means using the Euclidean distance measure for different values of k and create an elbow plot.
  • CEmbed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.
  • DRun K-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.
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• #107

  A Machine Learning Specialist is deciding between building a naive Bayesian model or a full Bayesian network for a classification problem. The Specialist computes the Pearson correlation coefficients between each feature and finds that their absolute values range between 0.1 to 0.95. Which model describes the underlying data in this situation?

  • AA naive Bayesian model, since the features are all conditionally independent.
  • BA full Bayesian network, since the features are all conditionally independent.
  • CA naive Bayesian model, since some of the features are statistically dependent.
  • DA full Bayesian network, since some of the features are statistically dependent.
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• #108

  An e commerce company wants to launch a new cloud-based product recommendation feature for its web application. Due to data localization regulations, any sensitive data must not leave its on-premises data center, and the product recommendation model must be trained and tested using nonsensitive data only. Data transfer to the cloud must use IPsec. The web application is hosted on premises with a PostgreSQL database that contains all the data. The company wants the data to be uploaded securely to Amazon S3 each day for model retraining. How should a machine learning specialist meet these requirements?

  • ACreate an AWS Glue job to connect to the PostgreSQL DB instance. Ingest tables without sensitive data through an AWS Site-to-Site VPN connection directly into Amazon S3.
  • BCreate an AWS Glue job to connect to the PostgreSQL DB instance. Ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job.
  • CUse AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3.
  • DUse PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection. Use AWS Glue to move data from Amazon EC2 to Amazon S3.
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• #109Pilih 2

  An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen. Which combination of algorithms would provide the appropriate insights? (Select TWO.)

  • AThe factorization machines (FM) algorithm.
  • BThe Latent Dirichlet Allocation (LDA) algorithm.
  • CThe Principal Component Analysis (PCA) algorithm.
  • DThe K-means algorithm.
  • EThe Random Cut Forest (RCF) algorithm.
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• #110Pilih 2

  A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations. The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is 99.1%, but the Data Scientist has been asked to reduce the number of false negatives. Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Choose two.)

  • AChange the XGBoost eval_metric parameter to optimize based on rmse instead of error.
  • BIncrease the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
  • CIncrease the XGBoost max_depth parameter because the model is currently underfitting the data.
  • DChange the XGBoost eval_metric parameter to optimize based on AUC instead of error.
  • EDecrease the XGBoost max_depth parameter because the model is currently overfitting the data.
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• #111

  A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue. Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to the automated so it runs once a week, starting Monday and completing by the close of business Friday. Which architecture should be used to scale the solution at the lowest cost?

  • AImplement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance.
  • BImplement the solution using a low-cost GPU compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task.
  • CImplement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler.
  • DImplement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler.
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• #112Pilih 3

  A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data. Which of the following methods should the Specialist consider using to correct this? (Choose three.)

  • ADecrease regularization.
  • BIncrease regularization.
  • CIncrease dropout.
  • DDecrease dropout.
  • EIncrease feature combinations.
  • FDecrease feature combinations.
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• #113

  A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance. How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

  • AInstall AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
  • BCreate a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
  • CUse the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.
  • DCreate an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
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• #114

  A Data Engineer needs to build a model using a dataset containing customer credit card information. How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

  • AUse a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers..
  • BUse an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
  • CUse an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker Principal Component Analysis (PCA) algorithm to reduce the length of the credit card numbers.
  • DUse AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
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• #115

  A Machine Learning Specialist is building a Logistic Regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold. What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?

  • AReceiver operating characteristic (ROC) curve.
  • BMisclassification rate.
  • CRoot Mean Square Error (RMSE).
  • DL1 norm.
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• #116

  A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day, the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL. Which storage scheme is MOST adapted to this scenario?

  • AStore datasets as files in Amazon S3.
  • BStore datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
  • CStore datasets as tables in a multi-node Amazon Redshift cluster.
  • DStore datasets as global tables in Amazon DynamoDB.
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• #117

  The displayed graph is from a forecasting model for testing a time series. Considering the graph only, which conclusion should a Machine Learning Specialist make about the behavior of the model?

  • AThe model predicts both the trend and the seasonality well.
  • BThe model predicts the trend well, but not the seasonality.
  • CThe model predicts the seasonality well, but not the trend.
  • DThe model does not predict the trend or the seasonality well.
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• #118

  A manufacturing company has a large set of labeled historical sales data. The manufacturer would like to predict how many units of a particular part should be produced each quarter. Which machine learning approach should be used to solve this problem?

  • ALogistic Regression.
  • BRandom Cut Forest (RCF).
  • CPrincipal Component Analysis (PCA).
  • DLinear Regression.
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• #119

  A company wants to predict the sale prices of houses based on available historical sales data. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable Linear Regression to predict house sale prices. Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

  • APlot a histogram of the features and compute their standard deviation. Remove features with high variance.
  • BPlot a histogram of the features and compute their standard deviation. Remove features with low variance.
  • CBuild a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.
  • DRun a correlation check of all features against the target variable. Remove features with low target variable correlation scores.
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• #120

  A manufacturer is operating a large number of factories with a complex supply chain relationship where unexpected downtime of a machine can cause production to stop at several factories. A data scientist wants to analyze sensor data from the factories to identify equipment in need of preemptive maintenance and then dispatch a service team to prevent unplanned downtime. The sensor readings from a single machine can include up to 200 data points including temperatures, voltages, vibrations, RPMs, and pressure readings. To collect this sensor data, the manufacturer deployed Wi-Fi and LANs across the factories. Even though many factory locations do not have reliable or high- speed internet connectivity, the manufacturer would like to maintain near-real-time inference capabilities. Which deployment architecture for the model will address these business requirements?

  • ADeploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
  • BDeploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
  • CDeploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.
  • DDeploy the model in Amazon SageMaker and use an IoT rule to write data to an Amazon DynamoDB table. Consume a DynamoDB stream from the table with an AWS Lambda function to invoke the endpoint.
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