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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) Exam is a certification exam offered by Amazon Web Services (AWS) for individuals who wish to demonstrate their expertise in machine learning on the AWS platform. AWS-Certified-Machine-Learning-Specialty Exam is designed to test the candidate's understanding of machine learning concepts, algorithms, data engineering, and data science practices on the AWS cloud.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q236-Q241):

NEW QUESTION # 236
A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has 15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

A. A regression forest where the number of trees is set equal to the number of product categories
B. An XGBoost model where the objective parameter is set to multi: softmax
C. A DeepAR forecasting model based on a recurrent neural network (RNN)
D. A deep convolutional neural network (CNN) with a softmax activation function for the last layer

Answer: B

Explanation:
XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post-processing the predictions. References:
XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.
XGBoost Parameters: A reference guide for the parameters of XGBoost.

NEW QUESTION # 237
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?

A. Principal component analysis (PCA)
B. Linear regression
C. Logistic regression
D. K-means

Answer: B

NEW QUESTION # 238
A Machine Learning Specialist wants to determine the appropriate SageMakerVariant Invocations Per Instance setting for an endpoint automatic scaling configuration. The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS As this is the first deployment, the Specialist intends to set the invocation safety factor to 0 5 Based on the stated parameters and given that the invocations per instance setting is measured on a per-minute basis, what should the Specialist set as the sageMakervariantinvocationsPerinstance setting?

A. 2,400
B. 0
C. 1
D. 2

Answer: B

NEW QUESTION # 239
A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:
March 1st, 2020, 08:14pm -
There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.
Which solution requires the LEAST operational overhead to create a new dataset with the added features?

A. Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
B. Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
C. Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.
D. Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

Answer: C

Explanation:
The solution C will create a new dataset with the added features with the least operational overhead because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution C involves the following steps:
Create a new flow in Amazon SageMaker Data Wrangler. A flow is a visual representation of the data preparation steps that can be applied to one or more datasets. The data scientist can create a new flow in the Amazon SageMaker Studio interface and import the S3 file as a data source1.
Use the Featurize date/time transform to generate the new variables. Amazon SageMaker Data Wrangler provides a set of preconfigured transformations that can be applied to the data with a few clicks. The Featurize date/time transform can parse a date/time column and generate new columns for the year, month, day, hour, minute, second, day of week, and day of year. The data scientist can use this transform to create the new variables from the timestamp variable2.
Save the dataset as a new file in Amazon S3. Amazon SageMaker Data Wrangler can export the transformed dataset as a new file in Amazon S3, or as a feature store in Amazon SageMaker Feature Store. The data scientist can choose the output format and location of the new file3.
The other options are not suitable because:
Option A: Creating an Amazon EMR cluster and developing PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the Amazon EMR cluster, the PySpark application, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing4.
Option B: Creating a processing job in Amazon SageMaker and developing Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the processing job, the Python code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing5.
Option D: Creating an AWS Glue job and developing code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3 will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to manage the AWS Glue job, the code, and the data storage. Moreover, the data scientist will have to write custom code for the date/time parsing and feature generation, which may require more development effort and testing6.
References:
1: Amazon SageMaker Data Wrangler
2: Featurize Date/Time - Amazon SageMaker Data Wrangler
3: Exporting Data - Amazon SageMaker Data Wrangler
4: Amazon EMR
5: Processing Jobs - Amazon SageMaker
6: AWS Glue

NEW QUESTION # 240
A machine learning (ML) specialist uploads 5 TB of data to an Amazon SageMaker Studio environment. The ML specialist performs initial data cleansing. Before the ML specialist begins to train a model, the ML specialist needs to create and view an analysis report that details potential bias in the uploaded data.
Which combination of actions will meet these requirements with the LEAST operational overhead? (Choose two.)

A. Configure SageMaker Data Wrangler to generate a bias report.
B. Use SageMaker Experiments to perform a data check
C. Turn on the bias detection option in SageMaker Ground Truth to automatically analyze data features.
D. Use SageMaker Clarify to automatically detect data bias
E. Use SageMaker Model Monitor to generate a bias drift report.

Answer: A,D

Explanation:
The combination of actions that will meet the requirements with the least operational overhead is to use SageMaker Clarify to automatically detect data bias and to configure SageMaker Data Wrangler to generate a bias report. SageMaker Clarify is a feature of Amazon SageMaker that provides machine learning (ML) developers with tools to gain greater insights into their ML training data and models. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias1. SageMaker Data Wrangler is another feature of Amazon SageMaker that enables you to prepare data for machine learning (ML) quickly and easily. You can use SageMaker Data Wrangler to identify potential bias during data preparation without having to write your own code. You specify input features, such as gender or age, and SageMaker Data Wrangler runs an analysis job to detect potential bias in those features. SageMaker Data Wrangler then provides a visual report with a description of the metrics and measurements of potential bias so that you can identify steps to remediate the bias2. The other actions either require more customization (such as using SageMaker Model Monitor or SageMaker Experiments) or do not meet the requirement of detecting data bias (such as using SageMaker Ground Truth). References:
1: Bias Detection and Model Explainability - Amazon Web Services
2: Amazon SageMaker Data Wrangler - Amazon Web Services

NEW QUESTION # 241
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AWS-Certified-Machine-Learning-Specialty Labs - AWS-Certified-Machine-Learning-Specialty Reliable Test Practice

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q236-Q241):

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