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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q24-Q29):

NEW QUESTION # 24
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable ecall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?

A. Train an anomaly detection model instead of an MLP
B. Add class weights to the MLP's loss function and then retrain
C. Gather more data using Amazon Mechanical Turk and then retrain
D. Train an XGBoost model instead of an MLP

Answer: B

Explanation:
The best technique to improve the recall of the MLP for the target class of interest is to add class weights to the MLP's loss function and then retrain. Class weights are a way of assigning different importance to each class in the dataset, such that the model will pay more attention to the classes with higher weights. This can help mitigate the class imbalance problem, where the model tends to favor the majority class and ignore the minority class. By increasing the weight of the target class of interest, the model will try to reduce the false negatives and increase the true positives, which will improve the recall metric. Adding class weights to the loss function is also a quick and easy solution, as it does not require gathering more data, changing the model architecture, or switching to a different algorithm.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Deep Learning with Amazon SageMaker
AWS Machine Learning Training - Class Imbalance and Weighted Loss Functions

NEW QUESTION # 25
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training.
The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.
B. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
C. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
D. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.

Answer: D

NEW QUESTION # 26
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings.
The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario? (Choose two.)

A. Linear regression
B. Random Forest classifier
C. Neural topic modeling (NTM)
D. Linear support vector machine
E. Latent Dirichlet allocation (LDA)

Answer: C,E

Explanation:
The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.
The other options are not suitable because:
* Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.
* Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.
* Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.
1: Latent Dirichlet Allocation
2: Neural Topic Modeling
3: Random Forest Classifier
4: Linear Support Vector Machine
5: Linear Regression

NEW QUESTION # 27
A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.
The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.
Which solution for text extraction and entity detection will require the LEAST amount of effort?

A. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
B. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
C. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
D. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.

Answer: A

NEW QUESTION # 28
A data scientist is using the Amazon SageMaker Neural Topic Model (NTM) algorithm to build a model that recommends tags from blog posts. The raw blog post data is stored in an Amazon S3 bucket in JSON format.
During model evaluation, the data scientist discovered that the model recommends certain stopwords such as
"a," "an," and "the" as tags to certain blog posts, along with a few rare words that are present only in certain blog entries. After a few iterations of tag review with the content team, the data scientist notices that the rare words are unusual but feasible. The data scientist also must ensure that the tag recommendations of the generated model do not include the stopwords.
What should the data scientist do to meet these requirements?

A. Use the SageMaker built-in Object Detection algorithm instead of the NTM algorithm for the training job to process the blog post data.
B. Run the SageMaker built-in principal component analysis (PCA) algorithm with the blog post data from the S3 bucket as the data source. Replace the blog post data in the S3 bucket with the results of the training job.
C. Remove the stop words from the blog post data by using the Count Vectorizer function in the scikit- learn library. Replace the blog post data in the S3 bucket with the results of the vectorizer.
D. Use the Amazon Comprehend entity recognition API operations. Remove the detected words from the blog post data. Replace the blog post data source in the S3 bucket.

Answer: C

Explanation:
The data scientist should remove the stop words from the blog post data by using the Count Vectorizer function in the scikit-learn library, and replace the blog post data in the S3 bucket with the results of the vectorizer. This is because:
* The Count Vectorizer function is a tool that can convert a collection of text documents to a matrix of token counts 1. It also enables the pre-processing of text data prior to generating the vector representation, such as removing accents, converting to lowercase, and filtering out stop words 1. By using this function, the data scientist can remove the stop words such as "a," "an," and "the" from the blog post data, and obtain a numerical representation of the text that can be used as input for the NTM algorithm.
* The NTM algorithm is a neural network-based topic modeling technique that can learn latent topics from a corpus of documents 2. It can be used to recommend tags from blog posts by finding the most probable topics for each document, and ranking the words associated with each topic 3. However, the NTM algorithm does not perform any text pre-processing by itself, so it relies on the quality of the input data. Therefore, the data scientist should replace the blog post data in the S3 bucket with the results of the vectorizer, to ensure that the NTM algorithm does not include the stop words in the tag recommendations.
* The other options are not suitable for the following reasons:
* Option A is not relevant because the Amazon Comprehend entity recognition API operations are used to detect and extract named entities from text, such as people, places, organizations, dates, etc4. This is not the same as removing stop words, which are common words that do not carry much meaning or information. Moreover, removing the detected entities from the blog post data may reduce the quality and diversity of the tag recommendations, as some entities may be relevant and useful as tags.
* Option B is not optimal because the SageMaker built-in principal component analysis (PCA) algorithm is used to reduce the dimensionality of a dataset by finding the most important features that capture the maximum amount of variance in the data 5. This is not the same as removing stop words, which are words that have low variance and high frequency in the data. Moreover, replacing the blog post data in the S3 bucket with the results of the PCA algorithm may not be compatible with the input format expected by the NTM algorithm, which requires a bag-of-words representation of the text 2.
* Option C is not suitable because the SageMaker built-in Object Detection algorithm is used to detect and localize objects in images 6. This is not related to the task of recommending tags from blog posts, which are text documents. Moreover, using the Object Detection algorithm instead of the NTM algorithm would require a different type of input data (images instead of text), and a different type of output data (bounding boxes and labels instead of topics and words).
Neural Topic Model (NTM) Algorithm
Introduction to the Amazon SageMaker Neural Topic Model
Amazon Comprehend - Entity Recognition
sklearn.feature_extraction.text.CountVectorizer
Principal Component Analysis (PCA) Algorithm
Object Detection Algorithm

NEW QUESTION # 29
......

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