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?

• A.Linear regression is inappropriate. The residuals do not have constant variance.
• B.Linear regression is inappropriate. The underlying data has outliers.
• C.Linear regression is appropriate. The residuals have constant variance.
• D.Linear regression is appropriate. The residuals have a zero mean.

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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.Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
• B.Use the SageMaker batch transform feature
• C.Compress the training data into Apache Parquet format.
• D.Ensure that the input mode for the training job is set to Pipe.

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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?

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

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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?

• A.Classification month-to-month using supervised learning of the 200 categories based on claim contents.
• B.Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
• C.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.
• D.Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.

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A machine learning (ML) specialist must develop a classification model for a financial services company. A domain expert provides the dataset, which is tabular with 10,000 rows and 1,020 features. During exploratory data analysis, the specialist finds no missing values and a small percentage of duplicate rows. There are correlation scores of > 0.9 for 200 feature pairs. The mean value of each feature is similar to its 50th percentile.
Which feature engineering strategy should the ML specialist use with Amazon SageMaker?

• A.Apply anomaly detection by using the Random Cut Forest (RCF) algorithm.
• B.Concatenate the features with high correlation scores by using a Jupyter notebook.
• C.Apply dimensionality reduction by using the principal component analysis (PCA) algorithm.
• D.Drop the features with low correlation scores by using a Jupyter notebook.

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