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Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is designed for professionals who want to demonstrate their expertise in the field of machine learning on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification is intended for individuals who have a strong understanding of AWS services and are looking to expand their skills and knowledge in machine learning.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q88-Q93):

NEW QUESTION # 88
A Machine Learning Specialist prepared the following graph displaying the results of k-means for k = [1:10]

Considering the graph, what is a reasonable selection for the optimal choice of k?

• A. 0
• B. 1
• C. 2
• D. 3

Answer: D

Explanation:
The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters.
References:
* Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.
* K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

 

NEW QUESTION # 89
A data scientist stores financial datasets in Amazon S3. The data scientist uses Amazon Athena to query the datasets by using SQL.
The data scientist uses Amazon SageMaker to deploy a machine learning (ML) model. The data scientist wants to obtain inferences from the model at the SageMaker endpoint However, when the data .... ntist attempts to invoke the SageMaker endpoint, the data scientist receives SOL statement failures The data scientist's 1AM user is currently unable to invoke the SageMaker endpoint Which combination of actions will give the data scientist's 1AM user the ability to invoke the SageMaker endpoint? (Select THREE.)

• A. Include the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query.
• B. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemaker: lnvokeEndpoint action,
• C. Include a policy statement for the data scientist's 1AM user that allows the 1AM user to perform the sagemakerGetRecord action.
• D. Perform a user remapping in SageMaker to map the 1AM user to another 1AM user that is on the hosted endpoint.
• E. Attach the AmazonAthenaFullAccess AWS managed policy to the user identity.
• F. Include an inline policy for the data scientist's 1AM user that allows SageMaker to read S3 objects

Answer: A,B,F

Explanation:
The correct combination of actions to enable the data scientist's IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:
* B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.
* C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.
* E: Including the SQL statement "USING EXTERNAL FUNCTION ml_function_name" in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.
The other options are not correct or necessary, because they have the following drawbacks:
* A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.
* D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:
GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.
* F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.
References:
* 1: InvokeEndpoint - Amazon SageMaker
* 2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena
* 3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog
* 4: AmazonAthenaFullAccess - AWS Identity and Access Management
* 5: GetRecord - Amazon SageMaker Feature Store Runtime
* : [Invoke a Multi-Model Endpoint - Amazon SageMaker]

 

NEW QUESTION # 90
A global bank requires a solution to predict whether customers will leave the bank and choose another bank.
The bank is using a dataset to train a model to predict customer loss. The training dataset has 1,000 rows. The training dataset includes 100 instances of customers who left the bank.
A machine learning (ML) specialist is using Amazon SageMaker Data Wrangler to train a churn prediction model by using a SageMaker training job. After training, the ML specialist notices that the model returns only false results. The ML specialist must correct the model so that it returns more accurate predictions.
Which solution will meet these requirements?

• A. Apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training.
• B. Apply undersampling to the training dataset before training.
• C. Apply normalization to the features of the training dataset before training.
• D. Apply anomaly detection to remove outliers from the training dataset before training.

Answer: A

Explanation:
The best solution to meet the requirements is to apply Synthetic Minority Oversampling Technique (SMOTE) to the training dataset before training. SMOTE is a technique that generates synthetic samples for the minority class by interpolating between existing samples. This can help balance the class distribution and provide more information to the model. SMOTE can improve the performance of the model on the minority class, which is the class of interest in churn prediction. SMOTE can be applied using the SageMaker Data Wrangler, which provides a built-in analysis for oversampling the minority class1.
The other options are not effective solutions for the problem. Applying anomaly detection to remove outliers from the training dataset before training may not improve the model's accuracy, as outliers may not be the main cause of the false results. Moreover, removing outliers may reduce the diversity of the data and make the model less robust. Applying normalization to the features of the training dataset before training may improve the model's convergence and stability, but it does not address the class imbalance issue. Normalization can also be applied using the SageMaker Data Wrangler, which provides a built-in transformation for scaling the features2. Applying undersampling to the training dataset before training may reduce the class imbalance, but it also discards potentially useful information from the majority class. Undersampling can also result in underfitting and high bias for the model.
References:
*Analyze and Visualize
*Transform and Export
*SMOTE for Imbalanced Classification with Python
*Churn prediction using Amazon SageMaker built-in tabular algorithms LightGBM, CatBoost, TabTransformer, and AutoGluon-Tabular

 

NEW QUESTION # 91
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?

• A. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
• B. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
• C. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
• D. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.

Answer: D

Explanation:
Explanation
To improve the training speed of a time-series forecasting model using TensorFlow, the Machine Learning Specialist should change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet1. Horovod can scale up to hundreds of GPUs with upwards of 90% scaling efficiency2. Horovod is easy to use, as it requires only a few lines of Python code to modify an existing training script2. Horovod is also portable, as it runs the same for TensorFlow, Keras, PyTorch, and MXNet; on premise, in the cloud, and on Apache Spark2.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly3. Amazon SageMaker supports Horovod as a built-in distributed training framework, which means that the Machine Learning Specialist does not need to install or configure Horovod separately4. Amazon SageMaker also provides a number of features and tools to simplify and optimize the distributed training process, such as automatic scaling, debugging, profiling, and monitoring4. By using Amazon SageMaker, the Machine Learning Specialist can parallelize the training to as many machines as needed to achieve the business goals, while minimizing coding effort and infrastructure changes.
References:
1: Horovod (machine learning) - Wikipedia
2: Home - Horovod
3: Amazon SageMaker - Machine Learning Service - AWS
4: Use Horovod with Amazon SageMaker - Amazon SageMaker

 

NEW QUESTION # 92
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

• A. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
• B. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
• C. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
• D. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

Answer: D

Explanation:
Collaborative filtering is a machine learning technique that recommends products or services to users based on the ratings or preferences of other users. This technique is well-suited for identifying customer shopping patterns and preferences because it takes into account the interactions between users and products.

 

NEW QUESTION # 93
......

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