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Earning the AWS Certified Machine Learning - Specialty certification demonstrates to employers and colleagues that you have the skills and knowledge needed to design and deploy machine learning models on the AWS platform. It can help you stand out in a competitive job market and increase your earning potential.

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. MLS-C01 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.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q96-Q101):

NEW QUESTION # 96
A Machine Learning Specialist is preparing data for training on Amazon SageMaker The Specialist is transformed into a numpy .array, which appears to be negatively affecting the speed of the training What should the Specialist do to optimize the data for training on SageMaker'?

A. Use AWS Glue to compress the data into the Apache Parquet format
B. Use the SageMaker hyperparameter optimization feature to automatically optimize the data
C. Transform the dataset into the Recordio protobuf format
D. Use the SageMaker batch transform feature to transform the training data into a DataFrame

Answer: C

NEW QUESTION # 97
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?

A. Area Under the ROC Curve (AUC)
B. Precision
C. Root Mean Square Error (RMSE)
D. Recall

Answer: B

NEW QUESTION # 98
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?

A. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
B. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
C. Run a SageMaker training job with an EFS file system as the data source.
D. Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.

Answer: A

NEW QUESTION # 99
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )

A. The Latent Dirichlet Allocation (LDA) algorithm
B. The factorization machines (FM) algorithm
C. The principal component analysis (PCA) algorithm
D. The Random Cut Forest (RCF) algorithm
E. The k-means algorithm

Answer: C,E

Explanation:
Explanation
The agency wants to analyze the census data for population segmentation, which is a type of unsupervised learning problem that aims to group similar data points together based on their attributes.
The agency can use a combination of algorithms that can perform dimensionality reduction and clustering on the data to achieve this goal.
Dimensionality reduction is a technique that reduces the number of features or variables in a dataset while preserving the essential information and relationships. Dimensionality reduction can help improve the efficiency and performance of clustering algorithms, as well as facilitate data visualization and interpretation. One of the most common algorithms for dimensionality reduction is principal component analysis (PCA), which transforms the original features into a new set of orthogonal features called principal components that capture the maximum variance in the data. PCA can help reduce the noise and redundancy in the data and reveal the underlying structure and patterns.
Clustering is a technique that partitions the data into groups or clusters based on their similarity or distance. Clustering can help discover the natural segments or categories in the data and understand their characteristics and differences. One of the most popular algorithms for clustering is k-means, which assigns each data point to one of k clusters based on the nearest mean or centroid. K-means can handle large and high-dimensional datasets and produce compact and spherical clusters.
Therefore, the combination of algorithms that would provide the appropriate insights for population segmentation are PCA and k-means. The agency can use PCA to reduce the dimensionality of the census data from 500 features to a smaller number of principal components that capture most of the variation in the data. Then, the agency can use k-means to cluster the data based on the principal components and identify the segments of the population that share similar characteristics.
References:
Amazon SageMaker Principal Component Analysis (PCA)
Amazon SageMaker K-Means Algorithm

NEW QUESTION # 100
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?

A. Increase the number of layers for the neural network.
B. Increase the dropout rate for the second-to-last layer.
C. Increase the number of epochs for model training.
D. Increase the training data by adding variation in rotation for training images.

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.
1: Image Augmentation - Amazon SageMaker

NEW QUESTION # 101
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