Amazon Amazon Certifications MLS-C01 Questions & Answers

• Question 351:

  An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

  A. . m5 4xlarge (general purpose)

  B. r5.2xlarge (memory optimized)

  C. p3.2xlarge (GPU accelerated computing)

  D. p3 8xlarge (GPU accelerated computing)

  Correct Answer: C

• Question 352:

  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?

  A. Run 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

  B. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team

  C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon

  D. S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

  E. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.

  Correct Answer: C

• Question 353:

  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?

  A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.

  B. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database

  C. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

  D. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database

  Correct Answer: C

  In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar.

  Amazon SageMaker Random Cut Forest (RCF) is an unsupervised algorithm for detecting anomalous data points within a data set

  Neural network is used for image detection.

• Question 354:

  A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:

  1.

  Must be accessible from a VPC only.

  2.

  Must not traverse the public internet. How can these requirements be satisfied?

  A. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.

  B. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

  C. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.

  D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

  Correct Answer: A

  In Option A, the Machine Learning Specialist would create a VPC endpoint for Amazon S3, which would allow traffic to flow directly between the VPC and Amazon S3 without traversing the public internet. Access to the S3 bucket containing PII can then be restricted to the VPC endpoint and the VPC using a bucket access policy. This would ensure that only instances within the VPC can access the data, and that the data does not traverse the public internet.

  Option B and D, allowing access from an Amazon EC2 instance, would not meet the requirement of not traversing the public internet, as the EC2 instance would be accessible from the internet. Option C, using Network Access Control Lists (NACLs) to allow traffic between only the VPC endpoint and an EC2 instance, would also not meet the requirement of not traversing the public internet, as the EC2 instance would still be accessible from the internet.

• Question 355:

  A Machine Learning Specialist needs to move and transform data in preparation for training. Some of the data needs to be processed in near-real time, and other data can be moved hourly. There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data.

  Which of the following services can feed data to the MapReduce jobs? (Choose two.)

  A. AWSDMS

  B. Amazon Kinesis

  C. AWS Data Pipeline

  D. Amazon Athena

  E. Amazon ES

  Correct Answer: BC

  https://docs.aws.amazon.com/datapipeline/latest/DeveloperGuide/what-is-datapipeline.html

  Amazon Kinesis (Option B) can be used to process data in near-real time, as it is a real-time data streaming service that can handle large amounts of incoming data from multiple sources. The data can be fed to Amazon EMR MapReduce

  jobs for processing.

  AWS Data Pipeline (Option C) can be used to move the hourly data, as it provides a way to move data from various sources to Amazon EMR for processing.

• Question 356:

  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?

  A. Streaming classification

  B. Binary classification

  C. Multi-category classification

  D. Regression classification

  Correct Answer: B

  Logistic regression will give the probability, and logistic regression is a binary classification algorithm. https://machinelearningmastery.com/logistic-regression-for-machine-learning/

• Question 357:

  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? (Select THREE.)

  A. Decrease regularization.

  B. Increase regularization.

  C. Increase dropout.

  D. Decrease dropout.

  E. Increase feature combinations.

  F. Decrease feature combinations.

  Correct Answer: BCF

  Increasing regularization helps to prevent overfitting by adding a penalty term to the loss function to discourage the model from learning the noise in the data. Increasing dropout helps to prevent overfitting by randomly dropping out some

  neurons during training, which forces the model to learn more robust representations that do not depend on the presence of any single neuron.

  Decreasing the number of feature combinations helps to simplify the model, making it less likely to overfit.

  https://docs.aws.amazon.com/machine-learning/latest/dg/model-fit-underfitting-vs-overfitting.html

• Question 358:

  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?

  A. Convert the records to Apache Parquet format

  B. Convert the records to JSON format

  C. Convert the records to GZIP CSV format

  D. Convert the records to XML format

  Correct Answer: A

  Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It's a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB. Reference: https://www.cloudforecast.io/blog/using-parquet-on-athena-to-save-money-on-aws/

• Question 359:

  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:

  1.

  Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.

  2.

  Support event-driven ETL pipelines.

  3.

  Provide a quick and easy way to understand metadata. Which approach meets trfese requirements?

  A. Use 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.

  B. Use 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.

  C. Use 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.

  D. Use 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.

  Correct Answer: A

  The AWS Glue Data Catalog is your persistent metadata store. It is a managed service that lets you store, annotate, and share metadata in the AWS Cloud in the same way you would in an Apache Hive metastore. The Data Catalog is a drop-in replacement for the Apache Hive Metastore

  https://docs.aws.amazon.com/zh_tw/glue/latest/dg/components-overview.html

• Question 360:

  A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model.

  What should the Specialist do to address the performance issues with the current solution?

  A. Use the SageMaker batch transform feature

  B. Compress the training data into Apache Parquet format.

  C. Ensure that the input mode for the training job is set to Pipe.

  D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

  Correct Answer: C