AWS Certified Machine Learning - Specialty

• #61Pilih 2

  An insurance company is developing a new device for vehicles that uses a camera to observe drivers' behavior and alert them when they appear distracted. The company created approximately 10,000 training images in a controlled environment that a Machine Learning Specialist will use to train and evaluate machine learning models. During the model evaluation, the Specialist notices that the training error rate diminishes faster as the number of epochs increases and the model is not accurately inferring on the unseen test images. Which of the following should be used to resolve this issue? (Choose two.)

  • AAdd vanishing gradient to the model.
  • BPerform data augmentation on the training data.
  • CMake the neural network architecture complex.
  • DUse gradient checking in the model.
  • EAdd L2 regularization to the model.
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• #62

  A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features. Which model will meet the business requirement?

  • ALogistic Regression.
  • BLinear Regression.
  • CK-means.
  • DPrincipal Component Analysis (PCA).
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• #63

  A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake. The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of: Real-time analytics. Interactive analytics of historical data. Clickstream analytics. Product recommendations. Which services should the Specialist use?

  • AAWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose
  • BAmazon Athena as the data catalog: Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-real-time data insights; Amazon Kinesis
  • CAWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose
  • DAmazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations.
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• #64

  A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters. Which approach will provide the MAXIMUM performance boost?

  • AInitialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
  • BUse gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
  • CReduce the learning rate and run the training process until the training loss stops decreasing.
  • DInitialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
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• #65Pilih 3

  A financial services company wants to adopt Amazon SageMaker as its default data science environment. The company's data scientists run machine learning (ML) models on confidential financial data. The company is worried about data egress and wants an ML engineer to secure the environment. Which mechanisms can the ML engineer use to control data egress from SageMaker? (Choose three.)

  • AConnect to SageMaker by using a VPC interface endpoint powered by AWS PrivateLink.
  • BUse SCPs to restrict access to SageMaker.
  • CDisable root access on the SageMaker notebook instances.
  • DEnable network isolation for training jobs and models.
  • ERestrict notebook presigned URLs to specific IPs used by the company.
  • FProtect data with encryption at rest and in transit. Use AWS Key Management Service (AWS KMS) to manage encryption keys.
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• #66

  A Machine Learning Specialist uploads a dataset to an Amazon S3 bucket protected with server-side encryption using AWS KMS. How should the ML Specialist define the Amazon SageMaker notebook instance so it can read the same dataset from Amazon S3?

  • ADefine security group(s) to allow all HTTP inbound/outbound traffic and assign those security group(s) to the Amazon SageMaker notebook instance.
  • BConfigure the Amazon SageMaker notebook instance to have access to the VPC. Grant permission in the KMS key policy to the notebook's KMS role.
  • CAssign an IAM role to the Amazon SageMaker notebook with S3 read access to the dataset. Grant permission in the KMS key policy to that role.
  • DAssign the same KMS key used to encrypt data in Amazon S3 to the Amazon SageMaker notebook instance.
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• #67

  An employee found a video clip with audio on a company's social media feed. The language used in the video is Spanish. English is the employee's first language, and they do not understand Spanish. The employee wants to do a sentiment analysis. What combination of services is the MOST efficient to accomplish the task?

  • AAmazon Transcribe, Amazon Translate, and Amazon Comprehend.
  • BAmazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
  • CAmazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
  • DAmazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText.
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• #68

  A Machine Learning Specialist is planning to create a long-running Amazon EMR cluster. The EMR cluster will have 1 master node, 10 core nodes, and 20 task nodes. To save on costs, the Specialist will use Spot Instances in the EMR cluster. Which nodes should the Specialist launch on Spot Instances?

  • AMaster node.
  • BAny of the core nodes.
  • CAny of the task nodes.
  • DBoth core and task nodes.
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• #69

  Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

  • ARecall.
  • BMisclassification rate.
  • CMean absolute percentage error (MAPE).
  • DArea Under the ROC Curve (AUC).
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• #70

  A data scientist has developed a machine learning translation model for English to Japanese by using Amazon SageMaker's built-in seq2seq algorithm with 500,000 aligned sentence pairs. While testing with sample sentences, the data scientist finds that the translation quality is reasonable for an example as short as five words. However, the quality becomes unacceptable if the sentence is 100 words long. Which action will resolve the problem?

  • AChange preprocessing to use n-grams.
  • BAdd more nodes to the Recurrent Neural Network (RNN) than the largest sentence's word count.
  • CAdjust hyperparameters related to the attention mechanism.
  • DChoose a different weight initialization type.
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• #71

  A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training. How should a data scientist adjust the solution?

  • AUse the event tracker in Amazon Personalize to include real-time user interactions.
  • BAdd user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
  • CImplement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
  • DAdd event type and event value fields to the interactions dataset in Amazon Personalize.
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• #72

  A Machine Learning Specialist is preparing data for training on Amazon SageMaker. The Specialist is using one of the SageMaker built-in algorithms for the training. The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training. What should the Specialist do to optimize the data for training on SageMaker?

  • AUse the SageMaker batch transform feature to transform the training data into a DataFrame.
  • BUse AWS Glue to compress the data into the Apache Parquet format.
  • CTransform the dataset into the RecordIO protobuf format.
  • DUse the SageMaker hyperparameter optimization feature to automatically optimize the data.
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• #73

  A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population. Which cross-validation strategy should the Data Scientist adopt?

  • AA k-fold cross-validation strategy with k=5.
  • BA stratified k-fold cross-validation strategy with k=5.
  • CA k-fold cross-validation strategy with k=5 and 3 repeats.
  • DAn 80/20 stratified split between training and validation.
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• #74

  A Machine Learning Specialist is attempting to build a Linear Regression model. Given the displayed residual plot only, what is the MOST likely problem with the model?

  • ALinear Regression is inappropriate. The residuals do not have constant variance.
  • BLinear Regression is inappropriate. The underlying data has outliers.
  • CLinear Regression is appropriate. The residuals have a zero mean.
  • DLinear Regression is appropriate. The residuals have constant variance.
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• #75

  A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements. Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum. Support event-driven ETL pipelines Provide a quick and easy way to understand metadata Which approach meets these requirements?

  • AUse an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data Catalog to search and discover metadata.
  • BUse an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
  • CUse an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
  • DUse an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
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• #76

  A retail chain has been ingesting purchasing records from its network of 20,000 stores to Amazon S3 using Amazon Kinesis Data Firehose. To support training an improved machine learning model, training records will require new but simple transformations, and some attributes will be combined. The model needs to be retrained daily. Given the large number of stores and the legacy data ingestion, which change will require the LEAST amount of development effort?

  • ARequire that the stores to switch to capturing their data locally on AWS Storage Gateway for loading into Amazon S3, then use AWS Glue to do the transformation.
  • BDeploy an Amazon EMR cluster running Apache Spark with the transformation logic, and have the cluster run each day on the accumulating records in Amazon S3, outputting new/transformed records to Amazon S3.
  • CSpin up a fleet of Amazon EC2 instances with the transformation logic, have them transform the data records accumulating on Amazon S3, and output the transformed records to Amazon S3.
  • DInsert an Amazon Kinesis Data Analytics stream downstream of the Kinesis Data Firehose stream that transforms raw record attributes into simple transformed values using SQL.
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• #77Pilih 2

  A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users. The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and 999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns. Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory. Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

  • AAdd more deep trees to the random forest to enable the model to learn more features.
  • BInclude a copy of the samples in the test dataset in the training dataset.
  • CGenerate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
  • DChange the cost function so that false negatives have a higher impact on the cost value than false positives.
  • EChange the cost function so that false positives have a higher impact on the cost value than false negatives.
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• #78

  A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website-for better service and smart recommendations. Which solution should the Specialist recommend?

  • ALatent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
  • BA neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
  • CCollaborative filtering based on user interactions and correlations to identify patterns in the customer database.
  • DRandom Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
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• #79

  A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only. The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases. Which action is recommended to provide the HIGHEST accuracy model for the company's test and validation data?

  • AIncrease the randomization of training data in the mini-batches used in training.
  • BAllocate a higher proportion of the overall data to the training dataset.
  • CApply L1 or L2 regularization and dropouts to the training.
  • DReduce the number of layers and units (or neurons) from the deep learning network.
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• #80Pilih 2

  A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker. What combination of services should the team use to build a custom algorithm in Amazon SageMaker? (Choose two.)

  • AAWS Secrets Manager.
  • BAWS CodeStar.
  • CAmazon ECR.
  • DAmazon ECS.
  • EAmazon S3.
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