AWS Certified Machine Learning - Specialty practice test

1. Question 1 · 1

   A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive. The model produces the following confusion matrix after evaluating on a test dataset of 100 customers: Based on the model evaluation results, why is this a viable model for production?

   Choose one answer.

   

   • The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
   • The precision of the model is 86%, which is less than the accuracy of the model.
   • The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
   • The precision of the model is 86%, which is greater than the accuracy of the model.

2. Question 2 · 1

   A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end- to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS. Which approach should the Specialist use for training a model using that data?

   Choose one answer.

   • Write a direct connection to the SQL database within the notebook and pull data in
   • Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
   • Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
   • Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.

3. Question 3 · 1

   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?

   Choose one answer.

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

4. Question 4 · 1

   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 be 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?

   Choose one answer.

   • Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance
   • Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task
   • Implement 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
   • Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler

5. Question 5 · 1

   A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1..10]: Considering the graph, what is a reasonable selection for the optimal choice of k?

   Choose one answer.

   

   • 1
   • 4
   • 7
   • 10

6. Question 6 · 1

   A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise. Which is the FASTEST route to index the assets?

   Choose one answer.

   • Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.
   • Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
   • Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.
   • Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.

7. Question 7 · 1

   A Machine Learning Specialist is working for an online retailer that wants to run analytics on every customer visit, processed through a machine learning pipeline. The data needs to be ingested by Amazon Kinesis Data Streams at up to 100 transactions per second, and the JSON data blob is 100 KB in size. What is the MINIMUM number of shards in Kinesis Data Streams the Specialist should use to successfully ingest this data?

   Choose one answer.

   • 1 shards
   • 10 shards
   • 100 shards
   • 1,000 shards

8. Question 8 · 1

   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?

   Choose one answer.

   • A naive Bayesian model, since the features are all conditionally independent.
   • A full Bayesian network, since the features are all conditionally independent.
   • A naive Bayesian model, since some of the features are statistically dependent.
   • A full Bayesian network, since some of the features are statistically dependent.

9. Question 9 · 1

   A Data Scientist is building a linear regression model and will use resulting p-values to evaluate the statistical significance of each coefficient. Upon inspection of the dataset, the Data Scientist discovers that most of the features are normally distributed. The plot of one feature in the dataset is shown in the graphic. What transformation should the Data Scientist apply to satisfy the statistical assumptions of the linear regression model?

   Choose one answer.

   

   • Exponential transformation
   • Logarithmic transformation
   • Polynomial transformation
   • Sinusoidal transformation

10. Question 10 · 1

    A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows. Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

    Choose one answer.

    

    • Increase the max_depth parameter value.
    • Lower the max_depth parameter value.
    • Update the objective to binary:logistic.
    • Lower the min_child_weight parameter value.

11. Question 11 · 1

    A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance. What type of machine learning model should be used?

    Choose one answer.

    • Classification month-to-month using supervised learning of the 200 categories based on claim contents.
    • Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
    • Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
    • Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

12. Question 12 · 1

    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?

    Choose one answer.

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

13. Question 13 · 1

    A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users. The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models. Which solution satisfies these requirements with MINIMAL effort?

    Choose one answer.

    • Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
    • Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
    • Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
    • Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.

14. Question 14 · 1

    An agricultural company is interested in using machine learning to detect specific types of weeds in a 100-acre grassland field. Currently, the company uses tractor-mounted cameras to capture multiple images of the field as 10 ֳ— 10 grids. The company also has a large training dataset that consists of annotated images of popular weed classes like broadleaf and non-broadleaf docks. The company wants to build a weed detection model that will detect specific types of weeds and the location of each type within the field. Once the model is ready, it will be hosted on Amazon SageMaker endpoints. The model will perform real-time inferencing using the images captured by the cameras. Which approach should a Machine Learning Specialist take to obtain accurate predictions?

    Choose one answer.

    • Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.
    • Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object- detection single-shot multibox detector (SSD) algorithm.
    • Prepare the images in RecordIO format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an object- detection single-shot multibox detector (SSD) algorithm.
    • Prepare the images in Apache Parquet format and upload them to Amazon S3. Use Amazon SageMaker to train, test, and validate the model using an image classification algorithm to categorize images into various weed classes.

15. Question 15 · 1

    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?

    Choose one answer.

    • Deploy the model in Amazon SageMaker. Run sensor data through this model to predict which machines need maintenance.
    • Deploy the model on AWS IoT Greengrass in each factory. Run sensor data through this model to infer which machines need maintenance.
    • Deploy the model to an Amazon SageMaker batch transformation job. Generate inferences in a daily batch report to identify machines that need maintenance.
    • Deploy 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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