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Amazon AWS-Certified-Machine-Learning-Specialty Questions - Latest Preparation Material [2025]

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Understanding functional and technical aspects of AWS Certified Machine Learning Specialty Exam Modeling

The following will be dicussed here:

• Frame business problems as machine learning problems
• Perform hyperparameter optimization
• Train machine learning models
• Evaluate machine learning models
• Select the appropriate model(s) for a given machine learning problem

>> AWS-Certified-Machine-Learning-Specialty Latest Test Materials <<

Amazon - Fantastic AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty Latest Test Materials

For Amazon professionals, passing the AWS Certified Machine Learning - Specialty exams such as the AWS-Certified-Machine-Learning-Specialty Exam is essential to achieve their dream professional life. However, passing the AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) Exam is not an easy task, especially for those with busy schedules who need time to prepare well for the AWS-Certified-Machine-Learning-Specialty Exam. To ensure success on the AWS-Certified-Machine-Learning-Specialty Exam, you need Amazon AWS-Certified-Machine-Learning-Specialty Exam Questions that contain all the relevant information about the exam.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q286-Q291):

NEW QUESTION # 286
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? (Choose two.)

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

Answer: C,E

Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.

 

NEW QUESTION # 287
A company sells thousands of products on a public website and wants to automatically identify products with potential durability problems. The company has 1.000 reviews with date, star rating, review text, review summary, and customer email fields, but many reviews are incomplete and have empty fields. Each review has already been labeled with the correct durability result.
A machine learning specialist must train a model to identify reviews expressing concerns over product durability. The first model needs to be trained and ready to review in 2 days.
What is the MOST direct approach to solve this problem within 2 days?

• A. Train a custom classifier by using Amazon Comprehend.
• B. Build a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet.
• C. Use a built-in seq2seq model in Amazon SageMaker.
• D. Train a built-in BlazingText model using Word2Vec mode in Amazon SageMaker.

Answer: A

Explanation:
Explanation
The most direct approach to solve this problem within 2 days is to train a custom classifier by using Amazon Comprehend. Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and syntax. Amazon Comprehend also provides a custom classification feature that allows users to create and train a custom text classifier using their own labeled data.
The custom classifier can then be used to categorize any text document into one or more custom classes. For this use case, the custom classifier can be trained to identify reviews that express concerns over product durability as a class, and use the star rating, review text, and review summary fields as input features. The custom classifier can be created and trained using the Amazon Comprehend console or API, and does not require any coding or machine learning expertise. The training process is fully managed and scalable, and can handle large and complex datasets. The custom classifier can be trained and ready to review in 2 days or less, depending on the size and quality of the dataset.
The other options are not the most direct approaches because:
Option B: Building a recurrent neural network (RNN) in Amazon SageMaker by using Gluon and Apache MXNet is a more complex and time-consuming approach that requires coding and machine learning skills. RNNs are a type of deep learning models that can process sequential data, such as text, and learn long-term dependencies between tokens. Gluon is a high-level API for MXNet that simplifies the development of deep learning models. Amazon SageMaker is a fully managed service that provides tools and frameworks for building, training, and deploying machine learning models. However, to use this approach, the machine learning specialist would have to write custom code to preprocess the data, define the RNN architecture, train the model, and evaluate the results. This would likely take more than
2 days and involve more administrative overhead.
Option C: Training a built-in BlazingText model using Word2Vec mode in Amazon SageMaker is not a suitable approach for text classification. BlazingText is a built-in algorithm in Amazon SageMaker that provides highly optimized implementations of the Word2Vec and text classification algorithms. The Word2Vec algorithm is useful for generating word embeddings, which are dense vector representations of words that capture their semantic and syntactic similarities. However, word embeddings alone are not sufficient for text classification, as they do not account for the context and structure of the text documents. To use this approach, the machine learning specialist would have to combine the word embeddings with another classifier model, such as a logistic regression or a neural network, which would add more complexity and time to the solution.
Option D: Using a built-in seq2seq model in Amazon SageMaker is not a relevant approach for text classification. Seq2seq is a built-in algorithm in Amazon SageMaker that provides a sequence-to-sequence framework for neural machine translation based on MXNet. Seq2seq is a supervised learning algorithm that can generate an output sequence of tokens given an input sequence of tokens, such as translating a sentence from one language to another. However, seq2seq is not designed for text classification, which requires assigning a label or a category to a text document, not generating another text sequence. To use this approach, the machine learning specialist would have to modify the seq2seq algorithm to fit the text classification task, which would be challenging and inefficient.
References:
Custom Classification - Amazon Comprehend
Build a Text Classification Model with Amazon Comprehend - AWS Machine Learning Blog Recurrent Neural Networks - Gluon API BlazingText Algorithm - Amazon SageMaker Sequence-to-Sequence Algorithm - Amazon SageMaker

 

NEW QUESTION # 288
A machine learning (ML) specialist is using the Amazon SageMaker DeepAR forecasting algorithm to train a model on CPU-based Amazon EC2 On-Demand instances. The model currently takes multiple hours to train.
The ML specialist wants to decrease the training time of the model.
Which approaches will meet this requirement7 (SELECT TWO )

• A. Replace On-Demand Instances with Spot Instances
• B. Use a pre-trained version of the model. Run incremental training.
• C. Replace CPU-based EC2 instances with GPU-based EC2 instances.
• D. Use multiple training instances.
• E. Configure model auto scaling dynamically to adjust the number of instances automatically.

Answer: C,D

Explanation:
The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:
* C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.
* D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.
The other options are not effective or relevant, because they have the following drawbacks:
* A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5. Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.
* B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.
* E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3. The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.
1: GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What's AI) Bouchard | Towards Data Science
2: How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog
3: DeepAR Forecasting Algorithm - Amazon SageMaker
4: Distributed Training - Amazon SageMaker
5: Managed Spot Training - Amazon SageMaker
6: Automatic Scaling - Amazon SageMaker
7: How the DeepAR Algorithm Works - Amazon SageMaker

 

NEW QUESTION # 289
A company wants to predict stock market price trends. The company stores stock market data each business day in Amazon S3 in Apache Parquet format. The company stores 20 GB of data each day for each stock code.
A data engineer must use Apache Spark to perform batch preprocessing data transformations quickly so the company can complete prediction jobs before the stock market opens the next day. The company plans to track more stock market codes and needs a way to scale the preprocessing data transformations.
Which AWS service or feature will meet these requirements with the LEAST development effort over time?

• A. AWS Glue jobs
• B. Amazon Athena
• C. AWS Lambda
• D. Amazon EMR cluster

Answer: A

Explanation:
AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating dat a. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1.
The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.
References:
1: AWS Glue - Fully Managed ETL Service - Amazon Web Services
2: Amazon EMR - Amazon Web Services
3: Amazon Athena - Interactive SQL Queries for Data in Amazon S3
[4]: AWS Lambda - Serverless Compute - Amazon Web Services

 

NEW QUESTION # 290
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

• A. Dataset shuffling is disabled
• B. The class distribution in the dataset is imbalanced
• C. The batch size is too big
• D. The learning rate is very high

Answer: D

Explanation:
Mini-batch gradient descent is a variant of gradient descent that updates the model parameters using a subset of the training data (called a mini-batch) at each iteration. The learning rate is a hyperparameter that controls how much the model parameters change in response to the gradient. If the learning rate is very high, the model parameters may overshoot the optimal values and oscillate around the minimum of the cost function.
This can cause the training accuracy to fluctuate and prevent the model from converging to a stable solution. To avoid this issue, the learning rate should be chosen carefully, such as by using a learning rate decay schedule or an adaptive learning rate algorithm1. Alternatively, the batch size can be increased to reduce the variance of the gradient estimates2. However, the batch size should not be too big, as this can slow down the training process and reduce the generalization ability of the model3. Dataset shuffling and class distribution are not likely to cause oscillations in training accuracy, as they do not affect the gradient updates directly. Dataset shuffling can help avoid getting stuck in local minima and improve the convergence speed of mini-batch gradient descent4. Class distribution can affect the performance and fairness of the model, especially if the dataset is imbalanced, but it does not necessarily cause fluctuations in training accuracy.

 

NEW QUESTION # 291
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