AWS Certified Machine Learning - Specialty

• #21

  A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data. Which solution requires the LEAST effort to be able to query this data?

  • AUse AWS Data Pipeline to transform the data and Amazon RDS to run queries.
  • BUse AWS Glue to catalogue the data and Amazon Athena to run queries.
  • CUse AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
  • DUse AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
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• #22

  A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU. Which architecture changes would ensure that provisioned resources are being utilized effectively?

  • ARedeploy the model as a batch transform job on an M5 instance.
  • BRedeploy the model on an M5 instance. Attach Amazon Elastic Inference to the instance.
  • CRedeploy the model on a P3dn instance.
  • DDeploy the model onto an Amazon Elastic Container Service (Amazon ECS) cluster using a P3 instance.
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• #23

  An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis. What approach would be the MOST effective to perform near-real time defect detection?

  • AUse AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from within AWS IoT Analytics to carry out analysis for anomalies.
  • BUse Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry out Apache Spark ML K-means clustering to determine anomalies.
  • CUse Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random Cut Forest (RCF) algorithm to determine anomalies.
  • DUse Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.
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• #24

  A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset. The company requires at least 85% accuracy to make use of the model. After an exhaustive grid search, the optimal hyperparameters produced the following: 68% accuracy on the training set. 67% accuracy on the validation set. What can the machine learning specialist do to improve the system's accuracy?

  • AUpload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.
  • BAdd more data to the training set and retrain the model using transfer learning to reduce the bias.
  • CUse a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
  • DTrain a new model using the current neural network architecture.
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• #25

  A city wants to monitor its air quality to address the consequences of air pollution. A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city. As this is a prototype, only daily data from the last year is available. Which model is MOST likely to provide the best results in Amazon SageMaker?

  • AUse the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
  • BUse Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.
  • CUse the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof regressor.
  • DUse the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_typeof classifier.
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• #26

  A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance. How should the records be stored in Amazon S3 to improve query performance?

  • ACSV files.
  • BParquet files.
  • CCompressed JSON.
  • DRecordIO.
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• #27

  A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora: Profiles for all past and existing customers. Profiles for all past and existing insured pets. Policy-level information. Premiums received Claims paid. What steps should be taken to implement a machine learning model to identify potential new customers on social media?

  • AUse regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
  • BUse clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
  • CUse a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
  • DUse a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
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• #28

  A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset. Which tool should be used to improve the validation accuracy?

  • AAmazon Comprehend syntax analysis and entity detection.
  • BAmazon SageMaker BlazingText cbow mode.
  • CNatural Language Toolkit (NLTK) stemming and stop word removal.
  • DScikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer.
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• #29

  A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily. The model accuracy is acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes. What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

  • ADo not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
  • BChange the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
  • CSwitch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
  • DMove the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
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• #30

  A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant. Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

  • AReview SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
  • BGenerate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
  • CBuild custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
  • DSend Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.
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• #31Pilih 3

  A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images. The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set. What changes should the Specialist consider to solve this issue? (Choose three.)

  • AChoose a higher number of layers.
  • BChoose a lower number of layers.
  • CChoose a smaller learning rate.
  • DEnable dropout.
  • EInclude all the images from the test set in the training set.
  • FEnable early stopping.
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• #32

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

  • ALoad a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
  • BLaunch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to the instance. Train on a small amount of the data to verify the training code and hyperparameters. Go back to Amazon SageMaker and train using the full dataset
  • CUse AWS Glue to train a model using a small subset of the data to confirm that the data will be compatible with Amazon SageMaker. Initiate a SageMaker training job using the full dataset from the S3 bucket using Pipe input mode.
  • DLoad a smaller subset of the data into the SageMaker notebook and train locally. Confirm that the training code is executing and the model parameters seem reasonable. Launch an Amazon EC2 instance with an AWS Deep Learning AMI and attach the S3 bucket to train the full dataset.
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• #33

  The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data. Which machine learning algorithm should the researchers use that BEST meets their requirements?

  • ALatent Dirichlet Allocation (LDA).
  • BRecurrent Neural Network (RNN).
  • CK-means.
  • DConvolutional Neural Network (CNN).
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• #34

  A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes. Which prior probability distribution should the ML Specialist use for this variable?

  • APoisson distribution.
  • BUniform distribution.
  • CNormal distribution.
  • DBinomial distribution.
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• #35Pilih 3

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

  • AAlexa for Business.
  • BAmazon Connect.
  • CAmazon Lex.
  • DAmazon Polly.
  • EAmazon Comprehend.
  • FAmazon Transcribe.
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• #36

  A machine learning specialist is developing a proof of concept for government users whose primary concern is security. The specialist is using Amazon SageMaker to train a Convolutional Neural Network (CNN) model for a photo classifier application. The specialist wants to protect the data so that it cannot be accessed and transferred to a remote host by malicious code accidentally installed on the training container. Which action will provide the MOST secure protection?

  • ARemove Amazon S3 access permissions from the SageMaker execution role.
  • BEncrypt the weights of the CNN model.
  • CEncrypt the training and validation dataset.
  • DEnable network isolation for training jobs.
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• #37

  A Machine Learning Specialist must build out a process to query a dataset on Amazon S3 using Amazon Athena. The dataset contains more than 800,000 records stored as plaintext CSV files. Each record contains 200 columns and is approximately 1.5 MB in size. Most queries will span 5 to 10 columns only. How should the Machine Learning Specialist transform the dataset to minimize query runtime?

  • AConvert the records to Apache Parquet format.
  • BConvert the records to JSON format.
  • CConvert the records to GZIP CSV format.
  • DConvert the records to XML format.
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• #38Pilih 3

  When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

  • AThe training channel identifying the location of training data on an Amazon S3 bucket.
  • BThe validation channel identifying the location of validation data on an Amazon S3 bucket.
  • CThe IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
  • DHyperparameters in a JSON array as documented for the algorithm used.
  • EThe Amazon EC2 instance class specifying whether training will be run using CPU or GP
  • FThe output path specifying where on an Amazon S3 bucket the trained model will persist.
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• #39

  A Machine Learning Specialist trained a regression model, but the first iteration needs optimizing. The Specialist needs to understand whether the model is more frequently overestimating or underestimating the target. What option can the Specialist use to determine whether it is overestimating or underestimating the target value?

  • ARoot Mean Square Error (RMSE).
  • BResidual plots.
  • CArea under the curve.
  • DConfusion matrix.
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• #40

  A company is running a machine learning prediction service that generates 100 TB of predictions every day. A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team. Which solution requires the LEAST coding effort?

  • ARun a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Give the Business team read-only access to S3.
  • BGenerate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team.
  • CRun a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3. Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team.
  • DGenerate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
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