Updated Amazon AWS-Certified-Machine-Learning-Specialty Dumps [2025] - Tips For Better Preparation

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam designed for individuals who want to demonstrate their expertise in machine learning on the AWS platform. AWS-Certified-Machine-Learning-Specialty exam is intended for professionals who have experience using AWS services for designing, building, and deploying machine learning solutions. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to design, implement, and deploy machine learning models using AWS services.

To be eligible for the AWS Certified Machine Learning - Specialty exam, candidates should have a minimum of one to two years of experience in machine learning and a solid understanding of AWS services and architecture. Additionally, candidates should be familiar with programming languages such as Python, R, and Java, and have experience working with data processing and analysis tools such as Apache Spark and TensorFlow. Passing AWS-Certified-Machine-Learning-Specialty Exam can help professionals showcase their skills and expertise in the field of machine learning and open up new career opportunities.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q133-Q138):

NEW QUESTION # 133
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

A. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is
20.85%*0.32
B. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
13.12%
C. The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47-
0.32)
D. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is
20.85%

Answer: B

NEW QUESTION # 134
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?

A. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
B. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group
C. Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
D. Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset

Answer: A

Explanation:
The best way to maintain the privacy and integrity of the data stored in Amazon S3 is to use a combination of VPC endpoints and S3 bucket policies. A VPC endpoint allows the SageMaker notebook instance to access the S3 bucket without going through the public internet. A bucket policy allows the S3 bucket owner to specify which VPCs or VPC endpoints can access the bucket. This way, the data is protected from unauthorized access and tampering. The other options are either insecure (A and D) or inefficient (B).
References: Using Amazon S3 VPC Endpoints, Using Bucket Policies and User Policies

NEW QUESTION # 135
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

A. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
B. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
C. Reduce the learning rate and run the training process until the training loss stops decreasing.
D. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.

Answer: B

Explanation:
Initializing the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector will provide the maximum performance boost for the LSTM model. Word2vec is a technique that learns distributed representations of words based on their co-occurrence in a large corpus of text. These representations capture semantic and syntactic similarities between words, which can help the LSTM model better understand the meaning and context of the sentences in the text documents. Using word2vec embeddings that are pretrained on a relevant domain (energy sector) can further improve the performance by reducing the vocabulary mismatch and increasing the coverage of the words in the text documents. References
:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Training - Text Classification with TF-IDF, LSTM, BERT: a comparison of performance
* AWS Machine Learning Training - Machine Learning - Exam Preparation Path

NEW QUESTION # 136
An ecommerce company is automating the categorization of its products based on images. A data scientist has trained a computer vision model using the Amazon SageMaker image classification algorithm. The images for each product are classified according to specific product lines. The accuracy of the model is too low when categorizing new products. All of the product images have the same dimensions and are stored within an Amazon S3 bucket. The company wants to improve the model so it can be used for new products as soon as possible.
Which steps would improve the accuracy of the solution? (Choose three.)

A. Use the Amazon Rekognition DetectLabels API to classify the products in the dataset.
B. Use Amazon Rekognition Custom Labels to train a new model.
C. Augment the images in the dataset. Use open-source libraries to crop, resize, flip, rotate, and adjust the brightness and contrast of the images.
D. Use the SageMaker semantic segmentation algorithm to train a new model to achieve improved accuracy.
E. Check whether there are class imbalances in the product categories, and apply oversampling or undersampling as required. Store the new dataset in Amazon S3.
F. Use a SageMaker notebook to implement the normalization of pixels and scaling of the images. Store the new dataset in Amazon S3.

Answer: B,C,E

Explanation:
Explanation
Option C is correct because augmenting the images in the dataset can help the model learn more features and generalize better to new products. Image augmentation is a common technique to increase the diversity and size of the training data.
Option E is correct because Amazon Rekognition Custom Labels can train a custom model to detect specific objects and scenes that are relevant to the business use case. It can also leverage the existing models from Amazon Rekognition that are trained on tens of millions of images across many categories.
Option F is correct because class imbalance can affect the performance and accuracy of the model, as it can cause the model to be biased towards the majority class and ignore the minority class. Applying oversampling or undersampling can help balance the classes and improve the model's ability to learn from the data.
Option A is incorrect because the semantic segmentation algorithm is used to assign a label to every pixel in an image, not to classify the whole image into a category. Semantic segmentation is useful for applications such as autonomous driving, medical imaging, and satellite imagery analysis.
Option B is incorrect because the DetectLabels API is a general-purpose image analysis service that can detect objects, scenes, and concepts in an image, but it cannot be customized to the specific product lines of the ecommerce company. The DetectLabels API is based on the pre-trained models from Amazon Rekognition, which may not cover all the categories that the company needs.
Option D is incorrect because normalizing the pixels and scaling the images are preprocessing steps that should be done before training the model, not after. These steps can help improve the model's convergence and performance, but they are not sufficient to increase the accuracy of the model on new products.
References:
1: Image Augmentation - Amazon SageMaker
2: Amazon Rekognition Custom Labels Features
3: [Handling Imbalanced Datasets in Machine Learning]
4: [Semantic Segmentation - Amazon SageMaker]
5: [DetectLabels - Amazon Rekognition]
6: [Image Classification - MXNet - Amazon SageMaker]
7: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
8: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
9: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
10: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
11: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
12: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
13: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
14: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]
15: [https://towardsdatascience.com/handling-imbalanced-datasets-in-machine-learning-7a0e84220f28]
16: [https://docs.aws.amazon.com/sagemaker/latest/dg/semantic-segmentation.html]
17: [https://docs.aws.amazon.com/rekognition/latest/dg/API_DetectLabels.html]
18: [https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html]

NEW QUESTION # 137
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

A. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR.
B. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
C. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR.
D. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR.

Answer: B

Explanation:
Explanation
A collaborative filtering recommendation engine is a type of machine learning system that can improve sales for a company by using the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users. A collaborative filtering recommendation engine works by finding the users who have similar ratings or preferences for the products, and then recommending the products that the similar users have liked but the target user has not seen or rated. A collaborative filtering recommendation engine can leverage the collective wisdom of the users and discover the hidden patterns and associations among the products and the users. A collaborative filtering recommendation engine can be implemented using Apache Spark ML on Amazon EMR, which are two services that can handle large-scale data processing and machine learning tasks. Apache Spark ML is a library that provides various tools and algorithms for machine learning, such as classification, regression, clustering, recommendation, etc. Apache Spark ML can run on Amazon EMR, which is a service that provides a managed cluster platform that simplifies running big data frameworks, such as Apache Spark, on AWS. Apache Spark ML on Amazon EMR can build a collaborative filtering recommendation engine using the Alternating Least Squares (ALS) algorithm, which is a matrix factorization technique that can learn the latent factors that represent the users and the products, and then use them to predict the ratings or preferences of the users for the products. Apache Spark ML on Amazon EMR can also support both explicit feedback, such as ratings or reviews, and implicit feedback, such as views or clicks, for building a collaborative filtering recommendation engine12

NEW QUESTION # 138
......

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Updated Amazon AWS-Certified-Machine-Learning-Specialty Dumps [2025] - Tips For Better Preparation

Reliable AWS-Certified-Machine-Learning-Specialty Exam Preparation - Valid Exam AWS-Certified-Machine-Learning-Specialty Book

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q133-Q138):