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What Topics Are Covered in AWS Machine Learning - Specialty Certification Exam?

The certification exam for the AWS Machine Learning – Specialty certification tests the candidates' ability to select the best machine learning strategy to improve the business processes. Also, they will be able to identify the best AWS service needed to implement different machine learning solutions. Besides, candidates will be able to design and put into practice reliable, scalable, and cost-optimized machine learning solutions. The AWS MLS-C01 exam, in particular, focuses on four domains. They are the following:

• Modeling;
• Data engineering;
• Exploratory Data Analysis;
• Machine Learning Implementation and Operations.

The first topic handles data engineering and has 3 sections. The first one handles the creation of data repositories for efficient machine learning strategies. Also, candidates will learn how to effectively identify and implement solutions related to data-ingestion. The third sub-domain included in this section focuses on the implementation and identification of different data-transformation solutions. The second tested area concentrates on exploratory data analysis. When they prepare for this topic, candidates will learn how to sanitize and prepare data for modeling. Also, they will learn how to make a performance when it comes to feature engineering. Finally, candidates will learn how to visualize data and analyze different parameters for machine learning. Within the modeling section, candidates will learn how to frame different business problems related to machine learning issues. Besides, they will find how to select the right models for different machine learning problems. In this section, candidates will also learn how to train effectively for machine learning models. Another essential concept that candidates will discover in this part is related to hyperparameter optimization performance. Last but not least, applicants will understand how to correctly evaluate machine learning models. The final domain concentrates on machine learning operations and implementation. This segment focuses on helping candidates develop advanced abilities in building machine learning solutions to achieve the highest performance and fault tolerance. These solutions will help them ensure availability, resilience, and scalability. Another subtopic included in the last objective deals with the recommendations and implementation of the right machine learning services adapted to the business context. Candidates will also learn how to apply fundamental AWS security practices to solve different machine learning issues. Finally, they will become ready to deploy and operationalize various machine learning solutions.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q158-Q163):

NEW QUESTION # 158
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

• A. Create a new feature space using principal component analysis (PCA)
• B. Perform one-hot encoding on highly correlated features
• C. Apply the Pearson correlation coefficient
• D. Use matrix multiplication on highly correlated features.

Answer: D
NEW QUESTION # 159
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?

• A. Modify the HPO configuration as follows:
  
  Select the most accurate hyperparameter configuration form this training job.
• B. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
  [0.01, 0.1]
  [0.001, 0.01]
  [0.0001, 0.001]
  Select the most accurate hyperparameter configuration form these three HPO jobs.
• C. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
  [0.01, 0.1]
  [0.001, 0.01]
  [0.0001, 0.001]
  Select the most accurate hyperparameter configuration form these three HPO jobs.
• D. Modify the HPO configuration as follows:
  
  Select the most accurate hyperparameter configuration form this HPO job.

Answer: A
NEW QUESTION # 160
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

• A. The output path specifying where on an Amazon S3 bucket the trained model will persist.
• B. Hyperparameters in a JSON array as documented for the algorithm used.
• C. The validation channel identifying the location of validation data on an Amazon S3 bucket.
• D. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
• E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
• F. The training channel identifying the location of training data on an Amazon S3 bucket.

Answer: A,D,E
NEW QUESTION # 161
A Machine Learning Specialist is designing a scalable data storage solution for Amazon SageMaker. There is an existing TensorFlow-based model implemented as a train.py script that relies on static training data that is currently stored as TFRecords.
Which method of providing training data to Amazon SageMaker would meet the business requirements with the LEAST development overhead?

• A. Use Amazon SageMaker script mode and use train.py unchanged. Put the TFRecord data into an Amazon S3 bucket. Point the Amazon SageMaker training invocation to the S3 bucket without reformatting the training data.
• B. Rewrite the train.py script to add a section that converts TFRecords to protobuf and ingests the protobuf data instead of TFRecords.
• C. Use Amazon SageMaker script mode and use train.py unchanged. Point the Amazon SageMaker training invocation to the local path of the data without reformatting the training data.
• D. Prepare the data in the format accepted by Amazon SageMaker. Use AWS Glue or AWS Lambda to reformat and store the data in an Amazon S3 bucket.

Answer: A Explanation:
https://github.com/aws-samples/amazon-sagemaker-script-mode/blob/master/tf-horovod-inference-pipeline/train.py
NEW QUESTION # 162
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. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
• C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
• D. 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.

Answer: D
NEW QUESTION # 163
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