Question 1

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?

Accepted Answer

A)  Streaming classification 
B)  Binary classification 
C)  Multi-category classification 
D)  Regression classification

Question 2

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 setting?

Accepted Answer

A)  10 
B)  30 
C)  600 
D)  2,400

Question 3

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?

Accepted Answer

A)  Store datasets as files in Amazon S3. 
B)  Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance. 
C)  Store datasets as tables in a multi-node Amazon Redshift cluster. 
D)  Store datasets as global tables in Amazon DynamoDB.

Question 4

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?

Accepted Answer

A)  Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training. 
B)  Change 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. 
C)  Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals. 
D)  Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.

Question 5

A machine learning specialist stores IoT soil sensor data in Amazon DynamoDB table and stores weather event data as JSON files in Amazon S3. The dataset in DynamoDB is 10 GB in size and the dataset in Amazon S3 is 5 GB in size. The specialist wants to train a model on this data to help predict soil moisture levels as a function of weather events using Amazon SageMaker. Which solution will accomplish the necessary transformation to train the Amazon SageMaker model with the LEAST amount of administrative overhead?

Accepted Answer

A)  Launch an Amazon EMR cluster. Create an Apache Hive external table for the DynamoDB table and S3 data. Join the Hive tables and write the results out to Amazon S3. 
B)  Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output to an Amazon Redshift cluster. 
C)  Enable Amazon DynamoDB Streams on the sensor table. Write an AWS Lambda function that consumes the stream and appends the results to the existing weather files in Amazon S3. 
D)  Crawl the data using AWS Glue crawlers. Write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3.

Question 6

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates. What is the MOST likely cause of this issue?

Accepted Answer

A)  The class distribution in the dataset is imbalanced. 
B)  Dataset shuffling is disabled. 
C)  The batch size is too big. 
D)  The learning rate is very high.

Question 7

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?

Accepted Answer

A)  Use 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. 
B)  Use 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. 
C)  Use 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 (PC
A)  algorithm to reduce the length of the credit card numbers. 
D)  Use 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.

Question 8

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?

Accepted Answer

A)  Root Mean Square Error (RMS
E)  
B)  Residual plots 
C)  Area under the curve 
D)  Confusion matrix

Question 9

A Mobile Network Operator is building an analytics platform to analyze and optimize a company's operations using Amazon Athena and Amazon S3. The source systems send data in .CSV format in real time. The Data Engineering team wants to transform the data to the Apache Parquet format before storing it on Amazon S3. Which solution takes the LEAST effort to implement?

Accepted Answer

A)  Ingest .CSV data using Apache Kafka Streams on Amazon EC2 instances and use Kafka Connect S3 to serialize data as Parquet 
B)  Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Glue to convert data into Parquet. 
C)  Ingest .CSV data using Apache Spark Structured Streaming in an Amazon EMR cluster and use Apache Spark to convert data into Parquet. 
D)  Ingest .CSV data from Amazon Kinesis Data Streams and use Amazon Kinesis Data Firehose to convert data into Parquet.

Question 10

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.)

Accepted Answer

A)  Add more deep trees to the random forest to enable the model to learn more features. 
B)  Include a copy of the samples in the test dataset in the training dataset. 
C)  Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data. 
D)  Change the cost function so that false negatives have a higher impact on the cost value than false positives. 
E)  Change the cost function so that false positives have a higher impact on the cost value than false negatives.

Question 11

A data scientist has explored and sanitized a dataset in preparation for the modeling phase of a supervised learning task. The statistical dispersion can vary widely between features, sometimes by several orders of magnitude. Before moving on to the modeling phase, the data scientist wants to ensure that the prediction performance on the production data is as accurate as possible. Which sequence of steps should the data scientist take to meet these requirements?

Accepted Answer

A)  Apply random sampling to the dataset. Then split the dataset into training, validation, and test sets. 
B)  Split the dataset into training, validation, and test sets. Then rescale the training set and apply the same scaling to the validation and test sets. 
C)  Rescale the dataset. Then split the dataset into training, validation, and test sets. 
D)  Split the dataset into training, validation, and test sets. Then rescale the training set, the validation set, and the test set independently.

Question 12

A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user. The data scientist is using AWS Glue to cleanse the company's application logs during ingestion. Which strategy will allow the data scientist to identify fraudulent accounts?

Accepted Answer

A)  Execute the built-in FindDuplicates Amazon Athena query. 
B)  Create a FindMatches machine learning transform in AWS Glue. 
C)  Create an AWS Glue crawler to infer duplicate accounts in the source data. 
D)  Search for duplicate accounts in the AWS Glue Data Catalog.

Question 13

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet. How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

Accepted Answer

A)  Create a NAT gateway within the corporate VPC. 
B)  Route Amazon SageMaker traffic through an on-premises network. 
C)  Create Amazon SageMaker VPC interface endpoints within the corporate VPC. 
D)  Create VPC peering with Amazon VPC hosting Amazon SageMaker.

Question 14

A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow. Which set of actions should the ML specialist take to meet these requirements?

Accepted Answer

A)  Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook. 
B)  Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. 
C)  Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR.    Attach the role     to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook. 
D)  Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.

Question 15

A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce. Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

Accepted Answer

A)  Cross-validation 
B)  Numerical value binning 
C)  High-degree polynomial transformation 
D)  Logarithmic transformation 
E)  One hot encoding

Question 16

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

Accepted Answer

A)  The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20.85% 
B)  The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12% 
C)  The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47-0.32) 
D)  The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20.85%*0.32

Question 17

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?

Accepted Answer

A)  A k-fold cross-validation strategy with k=5 
B)  A stratified k-fold cross-validation strategy with k=5 
C)  A k-fold cross-validation strategy with k=5 and 3 repeats 
D)  An 80/20 stratified split between training and validation

Question 18

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?

Accepted Answer

A)  Use the event tracker in Amazon Personalize to include real-time user interactions. 
B)  Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize. 
C)  Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker. 
D)  Add event type and event value fields to the interactions dataset in Amazon Personalize.

Question 19

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.)

Accepted Answer

A)  Add vanishing gradient to the model. 
B)  Perform data augmentation on the training data. 
C)  Make the neural network architecture complex. 
D)  Use gradient checking in the model. 
E)  Add L2 regularization to the model.

Question 20

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?

Accepted Answer

A)  Use AWS Data Pipeline to transform the data and Amazon RDS to run queries. 
B)  Use AWS Glue to catalogue the data and Amazon Athena to run queries. 
C)  Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries. 
D)  Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.

Exam 7: AWS Certified Machine Learning - Specialty (MLS-C01)

During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates. What is the MOST likely cause of this issue?

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?

A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet. How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

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?