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MLS-C01 Learning Material: AWS Certified Machine Learning - Specialty & MLS-C01 Practice Test

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Prerequisites

The potential candidates should have 12-24 months of experience in architecting, developing, or running machine learning or deep learning workloads particularly on AWS Cloud. They must also possess the ability to effectively express the intuition behind basic machine learning algorithms. It is also advisable to have some experience with machine learning and deep learning frameworks, as well as be able to follow operational & deployment best practices.

Amazon AWS-Certified-Machine-Learning-Specialty exam is an excellent certification program for professionals who want to build a career in machine learning or data science. It provides a comprehensive and in-depth understanding of the AWS platform and its machine learning services, and it validates an individual's knowledge and skills in designing, building, and deploying machine learning solutions on AWS.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q148-Q153):

NEW QUESTION # 148
A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.
.... Dh Amazon SageMaker algorithm will meet this requirement?

A. Semantic segmentation - MXNet
B. Image Classification - TensorFlow
C. XGBoost
D. Object Detection - TensorFlow

Answer: B

Explanation:
The best Amazon SageMaker algorithm for this task is Image Classification - TensorFlow. This algorithm is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub. Transfer learning allows the company to fine-tune one of the available pretrained models on their own dataset, even if a large amount of image data is not available. The image classification algorithm takes an image as input and outputs a probability for each provided class label. The company can choose from a variety of models, such as MobileNet, ResNet, or Inception, depending on their accuracy and speed requirements. The algorithm also supports distributed training, data augmentation, and hyperparameter tuning.
Image Classification - TensorFlow - Amazon SageMaker
Amazon SageMaker Provides New Built-in TensorFlow Image Classification Algorithm Image Classification with ResNet :: Amazon SageMaker Workshop Image classification on Amazon SageMaker | by Julien Simon - Medium

NEW QUESTION # 149
A city wants to monitor its air quality to address the consequences of air pollution A Machine Learning Specialist needs to forecast the air quality in parts per million of contaminates for the next 2 days in the city As this is a prototype, only daily data from the last year is available Which model is MOST likely to provide the best results in Amazon SageMaker?

A. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
B. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the single time series consisting of the full year of data with a predictor_type of regressor.
C. Use the Amazon SageMaker Linear Learner algorithm on the single time series consisting of the full year of data with a predictor_type of classifier.
D. Use Amazon SageMaker Random Cut Forest (RCF) on the single time series consisting of the full year of data.

Answer: A

NEW QUESTION # 150
This graph shows the training and validation loss against the epochs for a neural network The network being trained is as follows
* Two dense layers one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights

Which technique can be used to improve model performance in terms of accuracy in the validation set?

A. Adding another layer with the 100 neurons
B. Random initialization of weights with appropriate seed
C. Increasing the number of epochs
D. Early stopping

Answer: A

NEW QUESTION # 151
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

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

Answer: B,D,E

Explanation:
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, the common parameters that must be specified are:
* The training channel identifying the location of training data on an Amazon S3 bucket. This parameter tells SageMaker where to find the input data for the algorithm and what format it is in. For example, TrainingInputMode: File means that the input data is in files stored in S3.
* The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. This parameter grants SageMaker the necessary permissions to access the S3 buckets, ECR repositories, and other AWS resources needed for the training job. For example, RoleArn: arn:aws:iam::123456789012:
role/service-role/AmazonSageMaker-ExecutionRole-20200303T150948 means that SageMaker will use the specified role to run the training job.
* The output path specifying where on an Amazon S3 bucket the trained model will persist. This parameter tells SageMaker where to save the model artifacts, such as the model weights and parameters, after the training job is completed. For example, OutputDataConfig: {S3OutputPath:
s3://my-bucket/my-training-job} means that SageMaker will store the model artifacts in the specified S3 location.
The validation channel identifying the location of validation data on an Amazon S3 bucket is an optional parameter that can be used to provide a separate dataset for evaluating the model performance during the training process. This parameter is not required for all algorithms and can be omitted if the validation data is not available or not needed.
The hyperparameters in a JSON array as documented for the algorithm used is another optional parameter that can be used to customize the behavior and performance of the algorithm. This parameter is specific to each algorithm and can be used to tune the model accuracy, speed, complexity, and other aspects. For example, HyperParameters: {num_round: "10", objective: "binary:logistic"} means that the XGBoost algorithm will use 10 boosting rounds and the logistic loss function for binary classification.
The Amazon EC2 instance class specifying whether training will be run using CPU or GPU is not a parameter that is specified when submitting a training job using a built-in algorithm. Instead, this parameter is specified when creating a training instance, which is a containerized environment that runs the training code and algorithm. For example, ResourceConfig: {InstanceType: ml.m5.xlarge, InstanceCount: 1, VolumeSizeInGB:
10} means that SageMaker will use one m5.xlarge instance with 10 GB of storage for the training instance.
References:
* Train a Model with Amazon SageMaker
* Use Amazon SageMaker Built-in Algorithms or Pre-trained Models
* CreateTrainingJob - Amazon SageMaker Service

NEW QUESTION # 152
A data scientist obtains a tabular dataset that contains 150 correlated features with different ranges to build a regression model. The data scientist needs to achieve more efficient model training by implementing a solution that minimizes impact on the model's performance. The data scientist decides to perform a principal component analysis (PCA) preprocessing step to reduce the number of features to a smaller set of independent features before the data scientist uses the new features in the regression model.
Which preprocessing step will meet these requirements?

A. Reduce the dimensionality of the dataset by removing the features that have the lowest correlation. Load the data into Amazon SageMaker Data Wrangler. Perform a Min Max Scaler transformation step to scale the data. Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
B. Use the Amazon SageMaker built-in algorithm for PCA on the dataset to transform the data
C. Load the data into Amazon SageMaker Data Wrangler. Scale the data with a Min Max Scaler transformation step Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data.
D. Reduce the dimensionality of the dataset by removing the features that have the highest correlation Load the data into Amazon SageMaker Data Wrangler Perform a Standard Scaler transformation step to scale the data Use the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data

Answer: C

Explanation:
Explanation
Principal component analysis (PCA) is a technique for reducing the dimensionality of datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance. PCA is useful when dealing with datasets that have a large number of correlated features. However, PCA is sensitive to the scale of the features, so it is important to standardize or normalize the data before applying PCA. Amazon SageMaker provides a built-in algorithm for PCA that can be used to transform the data into a lower-dimensional representation. Amazon SageMaker Data Wrangler is a tool that allows data scientists to visually explore, clean, and prepare data for machine learning.
Data Wrangler provides various transformation steps that can be applied to the data, such as scaling, encoding, imputing, etc. Data Wrangler also integrates with SageMaker built-in algorithms, such as PCA, to enable feature engineering and dimensionality reduction. Therefore, option B is the correct answer, as it involves scaling the data with a Min Max Scaler transformation step, which rescales the data to a range of [0, 1], and then using the SageMaker built-in algorithm for PCA on the scaled dataset to transform the data. Option A is incorrect, as it does not involve scaling the data before applying PCA, which can affect the results of the dimensionality reduction. Option C is incorrect, as it involves removing the features that have the highest correlation, which can lead to information loss and reduce the performance of the regression model. Option D is incorrect, as it involves removing the features that have the lowest correlation, which can also lead to information loss and reduce the performance of the regression model. References:
Principal Component Analysis (PCA) - Amazon SageMaker
Scale data with a Min Max Scaler - Amazon SageMaker Data Wrangler
Use Amazon SageMaker built-in algorithms - Amazon SageMaker Data Wrangler

NEW QUESTION # 153
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