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To pass the exam, candidates must demonstrate proficiency in several key areas. These include designing, building, and deploying AWS machine learning solutions, as well as understanding how to apply machine learning to real-world business problems. AWS-Certified-Machine-Learning-Specialty exam also evaluates the candidate's knowledge of using AWS services and tools to build scalable, secure, and highly available machine learning models.
Amazon MLS-C01 Exam is a highly respected certification that validates the skills and knowledge of individuals who work with machine learning technologies on the AWS platform. By passing AWS-Certified-Machine-Learning-Specialty Exam, candidates can demonstrate their expertise in machine learning, as well as their ability to design, deploy, and maintain machine learning solutions on AWS.
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To become an AWS Certified Machine Learning - Specialty, you need to have a deep understanding of machine learning concepts, algorithms, and tools. You should also have practical experience in building and deploying machine learning models using AWS services such as Amazon SageMaker, AWS Lambda, Amazon Redshift, and Amazon Athena. AWS-Certified-Machine-Learning-Specialty Exam covers various topics such as data preparation, feature engineering, model training and deployment, optimization and tuning, and security and compliance. It consists of multiple-choice and multiple-response questions, and you have 170 minutes to complete it. Passing the exam requires a score of at least 750 out of 1000. By earning the AWS Certified Machine Learning - Specialty certification, you demonstrate your ability to design and deliver cutting-edge machine learning solutions on the AWS platform, which can open up new career opportunities and increase your earning potential.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q165-Q170):
NEW QUESTION # 165
A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS).
The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?
Answer: A
Explanation:
The simplest and fastest way to use the EFS dataset for SageMaker training is to run a SageMaker training job with an EFS file system as the data source. This option does not require any data copying or additional integration steps. SageMaker supports EFS as a data source for training jobs, and it can mount the EFS file system to the training container using the FileSystemConfig parameter. This way, the training script can access the data files as if they were on the local disk of the training instance. References:
* Access Training Data - Amazon SageMaker
* Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations) | AWS Machine Learning Blog
NEW QUESTION # 166
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?
Answer: D
Explanation:
To improve the test error for the image classifier, the Machine Learning Specialist should use the technique of increasing the training data by adding variation in rotation for training images. This technique is called data augmentation, which is a way of artificially expanding the size and diversity of the training dataset by applying various transformations to the original images, such as rotation, flipping, cropping, scaling, etc. Data augmentation can help the model learn more robust features that are invariant to the orientation, position, and size of the objects in the images. This can improve the generalization ability of the model and reduce the test error, especially for cases where the images are not well-aligned or have different perspectives1.
References:
1: Image Augmentation - Amazon SageMaker
NEW QUESTION # 167
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?
Answer: B
Explanation:
The plot in the graphic shows a right-skewed distribution, which violates the assumption of normality for linear regression. To correct this, the Data Scientist should apply a logarithmic transformation to the feature.
This will help to make the distribution more symmetric and closer to a normal distribution, which is a key assumption for linear regression. References:
* Linear Regression
* Linear Regression with Amazon Machine Learning
* Machine Learning on AWS
NEW QUESTION # 168
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?
Answer: D
NEW QUESTION # 169
A data scientist must build a custom recommendation model in Amazon SageMaker for an online retail company. Due to the nature of the company's products, customers buy only 4-5 products every 5-10 years. So, the company relies on a steady stream of new customers. When a new customer signs up, the company collects data on the customer's preferences. Below is a sample of the data available to the data scientist.
How should the data scientist split the dataset into a training and test set for this use case?
Answer: A
Explanation:
Explanation
The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data .
References:
Amazon SageMaker Developer Guide: Train and Test Datasets
Amazon Personalize Developer Guide: Preparing and Importing Data
NEW QUESTION # 170
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