Amazon AWS Certified AI Practitioner AIF-C0 Exam Dumps and Practice Test Questions Set 13 Q181-195

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Question 181

A company wants to automatically extract key financial values such as invoice numbers, payment amounts, and due dates from hundreds of scanned invoices. Which AWS service should they use?

A) Amazon Textract
B) Amazon Comprehend
C) AWS Glue
D) Amazon Rekognition

Answer: A) Amazon Textract

Explanation:

Amazon Comprehend focuses on natural language processing tasks such as identifying sentiment, extracting entities from text, and understanding language structure, but it does not specialize in extracting information from structured or semi-structured scanned documents. AWS Glue is a service designed to clean, catalog, and transform data, but it cannot interpret scanned images or extract text fields from them. Amazon Rekognition analyzes visual content such as faces, objects, and scenes but does not extract structured information from documents like invoices. Amazon Textract is specifically designed for processing scanned documents and detecting key text, forms, tables, and fields without requiring templates. It can automatically extract invoice numbers, payment terms, vendor names, totals, and dates even if the document layouts differ. Because Textract can interpret the relationships between fields and provide structured, machine-readable output, it becomes extremely useful for downstream financial workflows such as reconciliation, accounting automation, or audit processing. Unlike basic OCR engines, Textract uses machine learning to understand document context, making the extraction accurate and adaptable across varying invoice formats. This makes it the most suitable solution for organizations handling large volumes of invoices where manual entry would be time-consuming and prone to error. The ability to work at scale, integrate with automation workflows, and output consistent structured data firmly establishes Textract as the best choice for this use case.

Question 182

A business wants to build a voice-enabled customer support assistant that understands spoken questions and responds using synthesized speech. Which AWS services should be used together?

A) Amazon Lex and Amazon Polly
B) Amazon Transcribe and Amazon Rekognition
C) Amazon Comprehend and Amazon Textract
D) AWS Lambda and Amazon Translate

Answer: A) Amazon Lex and Amazon Polly

Explanation:

Amazon Transcribe converts speech to text but cannot conduct full conversational interactions or generate spoken replies. Amazon Rekognition focuses on analyzing images and videos, not speech or conversation. Amazon Comprehend analyzes written text for insights, but it does not handle interactive dialogues or speech output. Amazon Textract extracts text from documents and cannot participate in conversational AI. AWS Lambda executes functions without provisioning servers but does not provide conversational understanding or speech synthesis. Amazon Translate converts text between languages but cannot manage dialogue flows or create speech. Amazon Lex is tailored for conversational interfaces; it understands user intent, manages dialogue flow, and processes natural language input. Amazon Polly synthesizes lifelike speech, enabling the assistant to respond verbally. When combined, they create a complete voice-enabled support assistant capable of listening, understanding, and speaking responses. This pairing is ideal for customer support systems, virtual assistants, and voice-driven applications.

Question 183

A company wants to process streaming social media posts in real time to detect sudden changes in sentiment about their brand. Which AWS service should they use?

A) Amazon Kinesis Data Streams
B) Amazon Aurora
C) Amazon Translate
D) Amazon S3

Answer: A) Amazon Kinesis Data Streams

Explanation:

Amazon Aurora is a relational database engine optimized for transactional performance but cannot process streaming data in real time. Amazon Translate converts text between languages but does not manage or process continuous data streams. Amazon S3 stores objects at scale but does not provide capabilities for real-time ingestion or processing. Amazon Kinesis Data Streams ingests large volumes of streaming data and enables applications to analyze data as it arrives. When paired with downstream services such as Amazon Comprehend for sentiment analysis, Kinesis allows businesses to monitor social media conversations and detect abrupt sentiment shifts. This enables rapid response to emerging issues or public reactions. Kinesis is built for low-latency, high-throughput streaming analytics, making it ideal for real-time brand sentiment tracking.

Question 184

A data science team wants to automatically find patterns in unlabeled customer behavior data. Which machine learning approach is most appropriate?

A) Unsupervised learning
B) Supervised learning
C) Reinforcement learning
D) Transfer learning

Answer: A) Unsupervised learning

Explanation:

In the field of machine learning, selecting the correct learning paradigm is critical for effectively analyzing data and deriving actionable insights. The choice of approach largely depends on the nature of the data available and the specific objectives of the analysis. Different learning techniques—supervised learning, unsupervised learning, reinforcement learning, and transfer learning—have distinct characteristics and use cases. Understanding these differences is essential when the goal is to analyze customer behavior in a scenario where labeled datasets are unavailable.

Supervised learning is a widely used machine learning approach that depends on labeled datasets, where each input sample is associated with a known output or target. Models trained using supervised learning learn to map inputs to outputs and make predictions on new, unseen data. While supervised learning is highly effective for tasks such as fraud detection, sentiment analysis, or demand forecasting, it requires a sufficient volume of accurately labeled examples. In scenarios where labels do not exist, supervised learning is impractical because there is no reference to guide the model’s predictions or to measure its performance during training. Without labeled data, supervised models cannot learn the relationships needed to provide meaningful insights.

Reinforcement learning is another paradigm, distinct from both supervised and unsupervised learning. It focuses on training an agent to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties. Over time, the agent learns to optimize its actions to maximize cumulative rewards. Reinforcement learning is particularly suited for dynamic, sequential decision-making tasks, such as training robots, optimizing logistics routes, or playing complex games like chess or Go. However, it is not appropriate for scenarios involving static datasets where the objective is to analyze customer behavior or uncover patterns without a trial-and-error learning environment.

Transfer learning is a technique in which a model trained on one domain or task is adapted to perform a related task in another domain. Transfer learning can significantly reduce training time and improve performance when data in the target domain is limited. However, this approach still generally requires labeled data in the target domain for fine-tuning the model. In situations where labeled datasets are completely unavailable, transfer learning cannot be directly applied because there is no ground truth to guide the adaptation process.

Unsupervised learning, in contrast, is designed specifically for situations where labeled data is unavailable. Unsupervised learning algorithms analyze raw, unlabeled datasets to identify inherent structures, patterns, or relationships within the data. Techniques such as clustering, dimensionality reduction, and association rule learning are commonly employed in this paradigm. Clustering algorithms, for example, can group customers into segments based on purchasing behavior, demographic information, or engagement metrics, revealing hidden patterns that may not be obvious through manual analysis. Dimensionality reduction techniques can help identify the most significant factors influencing customer behavior, simplifying complex datasets while retaining meaningful structure. By focusing on pattern discovery rather than prediction of predefined labels, unsupervised learning allows organizations to extract valuable insights even in the absence of labeled data.

In scenarios where the goal is to explore and understand customer behavior without prior labeling, unsupervised learning is the most appropriate and effective approach. It enables analysts to uncover hidden structures, discover meaningful groupings, and identify correlations that can inform marketing strategies, product development, and customer engagement initiatives. By leveraging unsupervised learning, businesses can transform unlabeled data into actionable intelligence, providing a foundation for data-driven decision-making and further analytics efforts.

Question 185

A mobile app developer wants to add the ability to translate chat messages between users in real time. Which AWS service should they use?

A) Amazon Translate
B) Amazon Comprehend
C) Amazon Lex
D) Amazon Polly

Answer: A) Amazon Translate

Explanation:

In today’s globalized digital environment, businesses and organizations increasingly need to communicate with audiences across multiple languages. Applications such as multilingual chat platforms, customer support systems, and collaborative tools require real-time translation capabilities to ensure seamless communication. Choosing the appropriate service to meet these translation requirements is crucial, as different AWS services are optimized for distinct aspects of natural language processing and interaction. While several AWS tools provide capabilities related to text analysis, speech synthesis, or conversational interfaces, only certain services are designed specifically for translating text efficiently and accurately.

Amazon Comprehend is a natural language processing (NLP) service that focuses on understanding text. It can extract key phrases, detect sentiment, identify entities such as people, places, and organizations, and even recognize relationships within documents. Comprehend is highly effective for deriving insights from textual content and performing advanced language analysis. However, despite its powerful language understanding capabilities, it does not offer translation functionality. Comprehend cannot convert text from one language to another, which makes it unsuitable for scenarios requiring multilingual communication or real-time translation of messages.

Amazon Lex is another service that provides conversational AI capabilities. It allows developers to build chatbots and voice assistants that can interact with users in natural language. Lex supports features such as intent recognition, slot filling, and context management, enabling applications to carry on dynamic and interactive conversations. However, Lex’s capabilities are centered on chatbot logic and dialogue management. While it can understand user inputs and respond appropriately in a given language, it does not provide tools for translating text between different languages. Consequently, Lex alone cannot fulfill the requirements for real-time multilingual translation.

Amazon Polly focuses on text-to-speech conversion, enabling applications to generate natural-sounding speech from textual input. Polly supports a wide variety of voices and languages and is often used for accessibility, automated announcements, and interactive voice applications. While Polly can speak text in multiple languages, it does not perform translation. Text must already be in the target language for Polly to synthesize speech correctly. As such, Polly’s capabilities are limited to speech production rather than language conversion, and it does not address the need for translating messages in real time.

Amazon Translate, in contrast, is a neural machine translation service explicitly designed for converting text from one language to another. It provides fast, accurate, and scalable translation, supporting integration into real-time applications such as chat systems, customer service platforms, and collaborative tools. Translate continuously improves through machine learning, ensuring high-quality translations that preserve context and meaning. Its real-time translation capabilities allow applications to immediately convert messages between languages, enabling users who speak different languages to communicate seamlessly. This makes Amazon Translate the most appropriate service for scenarios requiring the translation of text in real time.

While Amazon Comprehend excels at natural language understanding, Amazon Lex builds interactive chatbots, and Amazon Polly generates speech from text, none of these services are designed to perform real-time text translation. Amazon Translate, by contrast, directly addresses the need for accurate, scalable, and integrated language translation. Its ability to process and convert messages instantly between multiple languages makes it the ideal choice for enabling multilingual communication in real-time messaging applications, ensuring that users can interact effectively regardless of their native language.

Question 186

A security team wants to discover sensitive personal information stored across thousands of files in Amazon S3. Which AWS service should they use?

A) Amazon Macie
B) Amazon Rekognition
C) AWS Glue
D) Amazon Textract

Answer: A) Amazon Macie

Explanation:

In today’s digital environment, organizations increasingly rely on cloud storage for their data, often storing large volumes of sensitive information in services such as Amazon S3. While cloud storage provides convenience and scalability, it also introduces security and compliance challenges, particularly when it comes to identifying and protecting personally identifiable information (PII) such as names, credit card numbers, social security numbers, and addresses. Detecting and classifying sensitive data in vast datasets is a critical requirement for businesses operating under data privacy regulations, and choosing the right AWS service for this task is essential.

Amazon Rekognition is a powerful service designed for analyzing images and videos. It can detect objects, faces, activities, text within images, and even inappropriate content. While Rekognition excels at visual analysis, its functionality is limited to image and video data and does not extend to identifying or classifying sensitive textual information stored in files on S3. It cannot automatically recognize patterns associated with personal information in structured or unstructured text formats, which makes it unsuitable for tasks involving PII detection.

AWS Glue is a service primarily focused on data integration, transformation, and preparation. It enables users to clean, normalize, and process datasets for downstream analytics and machine learning applications. While Glue is highly effective for transforming raw data into structured formats, it does not provide native capabilities for detecting or classifying sensitive information. Data scientists and analysts can write custom scripts in Glue to attempt identification of PII, but this approach requires significant expertise and does not provide the automated, scalable detection and classification that is often required for compliance purposes.

Amazon Textract is another AWS service that focuses on extracting text from scanned documents, images, and PDFs. It can identify printed and handwritten text, forms, and tables, transforming unstructured document content into machine-readable formats. Textract is highly effective for document digitization and structured data extraction. However, while it converts text from documents into usable formats, it does not analyze that text for sensitive content or classify PII. Without additional processing or integration with other services, Textract cannot automatically detect security or compliance risks associated with sensitive information.

Amazon Macie, in contrast, is purpose-built for the discovery, classification, and protection of sensitive data stored in Amazon S3. It leverages machine learning to automatically identify and categorize sensitive information such as names, addresses, credit card numbers, and other types of personal data. Macie continuously monitors S3 buckets to detect potential security risks, such as unencrypted sensitive files or unintended public access, and provides detailed alerts and reports. By automating PII discovery and classification, Macie significantly reduces the operational overhead and risk associated with manual data audits. It is specifically designed to address security and compliance needs, making it the most suitable AWS service for organizations that need to scan S3 for sensitive personal information efficiently and accurately.

, while Amazon Rekognition excels at image and video analysis, AWS Glue focuses on data transformation, and Amazon Textract converts document content into structured text, none of these services provide automated, scalable detection of sensitive information. Amazon Macie uniquely addresses this challenge by leveraging machine learning to identify and classify personally identifiable information stored in S3. Its ability to monitor, detect, and report on sensitive data ensures that organizations can maintain compliance, reduce risk, and safeguard critical information, making it the ideal solution for scanning cloud storage for sensitive personal data.

Question 187

A bank wants to detect unusual withdrawal patterns that may indicate fraudulent activity, using historical customer transaction data. Which AWS service simplifies this process?

A) Amazon Lookout for Metrics
B) Amazon Translate
C) Amazon Polly
D) Amazon Textract

Answer: A) Amazon Lookout for Metrics

Explanation:

In modern business environments, monitoring for unusual activity, detecting anomalies, and identifying potential fraud are critical tasks that require specialized tools capable of analyzing complex datasets efficiently. Businesses generate large volumes of data from transactions, operational processes, and customer interactions. Detecting irregular patterns within this data is essential to prevent financial loss, maintain compliance, and ensure operational integrity. Choosing the right service for anomaly detection depends on understanding the capabilities of various AWS tools and how they apply to different types of data analysis.

Amazon Translate is a neural machine translation service designed to convert text from one language to another. It supports real-time and batch translation, enabling multilingual communication across applications. While it is highly effective for bridging language barriers in chat systems, documents, and collaborative platforms, its capabilities are entirely focused on text conversion. Translate does not provide analytics functionality, nor can it identify anomalies in financial transactions, operational metrics, or customer behavior. Consequently, it is not suitable for fraud detection or any scenario requiring pattern recognition in numeric or transactional data.

Amazon Polly, on the other hand, is a text-to-speech service that converts written content into natural-sounding spoken audio. Polly supports multiple voices and languages, making it ideal for applications such as accessibility, automated announcements, or interactive voice interfaces. However, Polly’s primary functionality is speech synthesis. It does not perform data analysis, metric evaluation, or anomaly detection, which are essential for identifying unusual activity in business operations. While Polly can vocalize textual information, it cannot evaluate the underlying data to detect irregularities or potential fraud.

Amazon Textract is designed to extract text, tables, and structured information from scanned documents, PDFs, and images. Textract excels in converting unstructured document content into machine-readable data, facilitating document processing workflows, and automating data entry. While it is valuable for extracting information from invoices, forms, or reports, it does not analyze numeric data or business metrics to identify anomalies. Textract provides structured text outputs but does not offer insights into patterns, deviations, or unusual activity that might indicate fraudulent behavior.

Amazon Lookout for Metrics is specifically built for detecting anomalies in business metrics using machine learning. This service is designed to automatically monitor operational and financial data, such as transaction amounts, customer behavior metrics, sales figures, or website engagement statistics, and identify deviations from expected patterns. Lookout for Metrics leverages advanced algorithms to surface anomalies that might indicate fraud, errors, or operational inefficiencies. It can handle time-series data, account for seasonal trends, and adapt to changing business conditions, providing actionable insights with minimal configuration. Users receive diagnostics that help explain the root causes of anomalies, enabling faster response and mitigation. Its integration with dashboards and alerting systems allows organizations to continuously monitor key performance indicators and operational data in real time.

, while Amazon Translate, Amazon Polly, and Amazon Textract provide powerful capabilities in translation, speech synthesis, and document processing, they are not suitable for detecting irregularities in business or operational data. Amazon Lookout for Metrics, however, is purpose-built for anomaly detection in numeric datasets and operational metrics. Its machine learning-driven approach to identifying unusual patterns makes it the ideal choice for organizations seeking to detect fraud, uncover operational issues, or monitor critical business indicators efficiently and effectively.

while Amazon Translate, Amazon Polly, and Amazon Textract provide powerful capabilities in translation, speech synthesis, and document processing, they are not suitable for detecting irregularities in business or operational data. Amazon Lookout for Metrics, however, is purpose-built for anomaly detection in numeric datasets and operational metrics. Its machine learning-driven approach to identifying unusual patterns makes it the ideal choice for organizations seeking to detect fraud, uncover operational issues, or monitor critical business indicators efficiently and effectively.

Question 188

A company wants a simple way to explore and clean a large dataset without writing code before using it for ML model building. Which AWS service fits this need?

A) AWS Glue DataBrew
B) Amazon SageMaker
C) Amazon Redshift
D) Amazon S3

Answer: A) AWS Glue DataBrew

Explanation:

In the modern data-driven landscape, organizations must ensure that their datasets are clean, consistent, and well-prepared before feeding them into machine learning models or performing advanced analytics. Proper data preparation is essential because the quality of the data directly affects the accuracy and reliability of insights, predictions, and business decisions. While AWS provides a wide range of services for data storage, analytics, and machine learning, not all of these services are designed to simplify the process of cleaning, exploring, and transforming raw data. Understanding the distinctions between these services is crucial for selecting the right tool for code-free data preparation.

Amazon SageMaker is a comprehensive service for building, training, and deploying machine learning models at scale. It provides tools for feature engineering, model training, hyperparameter tuning, and deployment pipelines. While SageMaker is highly versatile and powerful for creating machine learning models, it does not inherently offer a low-code, visual interface for data cleaning and preprocessing. Users who want to prepare datasets for training must either write custom scripts or use additional tools, which may require significant programming expertise. Consequently, SageMaker’s strength lies in model development rather than streamlining dataset preparation for non-technical users.

Amazon Redshift is a fully managed data warehouse that allows organizations to run large-scale analytics on structured data. It is optimized for querying, reporting, and aggregating massive datasets using SQL, making it an excellent choice for business intelligence and analytics workflows. However, Redshift is not designed for file-level data preparation, such as cleaning messy records, normalizing values, handling missing data, or performing exploratory transformations. Its functionality focuses on storing and querying data efficiently, but it does not provide visual tools or low-code operations for preparing datasets in a user-friendly way.

Amazon S3 serves as highly durable, scalable cloud storage for objects and datasets. It is ideal for storing raw data from multiple sources, including structured and unstructured formats. While S3 is excellent for centralized data storage, it does not include built-in capabilities to clean, transform, or explore data. Users can store large volumes of data safely, but S3 alone cannot convert inconsistent datasets into formats suitable for machine learning or analytics without the use of external processing tools.

AWS Glue DataBrew addresses these gaps by offering a visual, code-free interface for dataset preparation. DataBrew allows users to clean, normalize, and transform data using over 250 prebuilt transformations without writing any code. It supports a wide range of operations, including removing duplicates, correcting data types, standardizing text, handling null values, filtering records, and enriching data. Additionally, DataBrew provides profiling capabilities that allow users to identify anomalies, patterns, and inconsistencies in datasets. This visual interface simplifies data exploration, making it accessible to both technical and non-technical users. By integrating with other AWS services, DataBrew ensures that cleaned and transformed data can seamlessly flow into analytics pipelines, data warehouses, or machine learning workflows.

while SageMaker excels in model building, Redshift is optimized for analytics, and S3 provides scalable storage, none of these services offer a code-free, visual approach to preparing datasets. AWS Glue DataBrew uniquely fills this role by allowing organizations to clean, explore, and transform datasets efficiently, making it the ideal choice for low-code data preparation. Its extensive built-in operations, profiling tools, and visual interface enable users to prepare high-quality data for downstream analytics or machine learning tasks, significantly reducing the time, effort, and technical expertise required for effective data preparation.

Question 189

A company wants to search through large volumes of internal documents to find specific answers to employee questions. Which AWS AI service is best suited for this?

A) Amazon Kendra
B) Amazon Lex
C) Amazon Rekognition
D) AWS Glue

Answer: A) Amazon Kendra

Explanation:

Amazon Lex responds to user queries but does not search document repositories. Amazon Rekognition analyzes images, not text documents. AWS Glue performs ETL tasks but does not provide intelligent search. Amazon Kendra is an enterprise search service using machine learning to understand natural language queries and retrieve precise answers from corporate documents. This makes it the best choice for internal knowledge search.

Question 190

A company wants to identify objects, animals, and scenes in millions of product photos automatically. Which AWS service should they use?

A) Amazon Rekognition
B) Amazon Textract
C) Amazon Transcribe
D) Amazon Aurora

Answer: A) Amazon Rekognition

Explanation:

In the modern digital marketplace, e-commerce platforms and retail businesses increasingly rely on image analysis to manage, catalog, and optimize their product listings. Accurate analysis of product photos is essential for a variety of tasks, including quality control, automated tagging, content moderation, visual search, and inventory management. Selecting the right technology for analyzing product images requires an understanding of the capabilities and limitations of the available AWS services.

Amazon Textract is a service designed to extract structured information from scanned documents, PDFs, and forms. Textract excels at identifying text, tables, and key-value pairs in documents and converting unstructured content into machine-readable formats. While this functionality is highly useful for document processing and data entry automation, it is not intended for image recognition in photographs. Textract cannot detect visual elements such as objects, colors, textures, or scenes in product images, and therefore cannot support applications that require understanding the content of visual media.

Amazon Transcribe is a service that converts spoken language into written text. It is commonly used for generating transcripts from audio or video content, such as meeting recordings, podcasts, or customer service calls. Although Transcribe is powerful for handling speech-to-text tasks, it does not provide any functionality for analyzing visual content. As a result, it cannot be used to recognize or categorize objects in product photographs or perform any form of image analysis.

Amazon Aurora is a relational database service designed for high-performance, scalable storage and retrieval of structured data. Aurora supports transactional workloads, analytics, and complex queries with high reliability. While it is essential for storing and managing data, it does not provide any capabilities for analyzing images. Databases like Aurora can store metadata about images or links to image files, but they do not analyze visual content themselves.

Amazon Rekognition, in contrast, is explicitly designed for image and video analysis. It can identify objects, scenes, activities, and other visual elements within photographs and videos. Rekognition offers features such as object and scene detection, facial analysis, celebrity recognition, and unsafe content detection. It can automatically tag images, allowing businesses to organize and search large image libraries efficiently. Its ability to scale automatically makes it well-suited for companies managing extensive catalogs of product photos, enabling batch processing and real-time analysis without manual intervention. This combination of scalability and powerful visual recognition capabilities ensures that businesses can accurately categorize, filter, and analyze visual content at scale.

For applications such as e-commerce, where product images must be processed to extract relevant visual information, Rekognition provides the most appropriate solution. It allows platforms to automate catalog management, implement visual search capabilities, monitor image quality, and detect inappropriate or irrelevant content. By leveraging machine learning to interpret visual data, Rekognition enables organizations to gain insights from product imagery that would otherwise require significant manual effort.

while Amazon Textract, Amazon Transcribe, and Amazon Aurora provide valuable capabilities in document processing, speech-to-text conversion, and database management, they are not suitable for analyzing product images. Amazon Rekognition, with its robust object detection, scene recognition, and scalable architecture, is the ideal service for understanding, organizing, and leveraging visual content in large collections of product photos. Its features make it a critical tool for businesses seeking to automate image analysis and enhance their visual content workflows.

Question 191

A team wants to generate text summaries of long documents without building their own NLP model. Which AWS service can help?

A) Amazon Comprehend
B) Amazon Textract
C) Amazon Kinesis
D) Amazon Personalize

Answer: A) Amazon Comprehend

Explanation:

In today’s data-driven world, organizations are inundated with vast amounts of textual information, ranging from reports, emails, and customer feedback to social media content and internal documentation. Efficiently processing and understanding this content is critical for businesses that aim to extract actionable insights, streamline operations, and improve decision-making. One of the key challenges in managing large volumes of text is the ability to automatically summarize content, condensing it into essential points without losing critical information. Choosing the right technology to accomplish this requires an understanding of the capabilities of various AWS services and their applicability to text analysis and summarization tasks.

Amazon Textract is a service designed to extract text, tables, and structured data from scanned documents, PDFs, and forms. Its primary strength lies in converting unstructured text and document elements into machine-readable formats. Textract excels at document digitization and data extraction, making it ideal for scenarios where structured data needs to be pulled from invoices, forms, or reports. However, Textract does not provide capabilities for analyzing, interpreting, or summarizing the extracted text. While it can capture the content, organizations would still need to implement additional processing to condense the information into summaries or extract key insights.

Amazon Kinesis is a real-time data streaming service that enables ingestion, processing, and analysis of high-velocity data streams. It is widely used for monitoring logs, metrics, and event-driven applications. While Kinesis is highly effective for handling continuous streams of data and enabling real-time analytics, it does not offer capabilities for text understanding, natural language processing, or summarization. Its focus is on moving and processing large-scale data streams, not analyzing or condensing textual content.

Amazon Personalize provides machine learning-powered recommendation systems. It helps organizations deliver personalized experiences, such as product recommendations, content suggestions, or targeted marketing offers. While Personalize uses advanced algorithms to model user behavior and preferences, it is not designed to process or summarize textual content. Its capabilities are specific to predictive personalization and do not extend to natural language understanding or content condensation.

Amazon Comprehend, in contrast, is a natural language processing service designed to extract insights and understand text automatically. It provides a range of text analytics features, including entity recognition, key phrase extraction, sentiment analysis, language detection, and, importantly, automatic text summarization. Comprehend can process documents or large text corpora and generate concise summaries that capture the essential information, eliminating the need for custom model development. Its ability to summarize content automatically makes it highly suitable for scenarios where businesses need to condense long documents, reports, or articles into digestible insights efficiently. Comprehend’s machine learning models are pre-trained and scalable, allowing organizations to process text at scale with minimal setup.

while Amazon Textract, Amazon Kinesis, and Amazon Personalize each provide valuable capabilities in document text extraction, real-time data streaming, and recommendation systems respectively, they do not fulfill the requirement of automatically summarizing textual content. Amazon Comprehend, with its comprehensive text analytics and summarization capabilities, is the service specifically designed to understand and condense text effectively. It enables organizations to extract meaningful insights from large volumes of text, providing concise and accurate summaries that support faster decision-making, improved content management, and better overall efficiency. This makes Comprehend the ideal solution for automatic text summarization in a variety of business contexts.

Question 192

A robotics company wants to train a robot to navigate a maze by rewarding correct moves. Which machine learning technique is suitable?

A) Reinforcement learning
B) Supervised learning
C) Unsupervised learning
D) Regression analysis

Answer: A) Reinforcement learning

Explanation:

Supervised learning uses labeled data and cannot teach decision-making through rewards. Unsupervised learning finds patterns but does not optimize actions. Regression predicts numeric values but does not guide behavior. Reinforcement learning uses rewards and penalties to train agents to make optimal decisions. This makes it ideal for training robots to navigate environments.

Question 193

A healthcare company wants to classify pathology images into diagnostic categories without building a deep learning model from scratch. Which AWS service should they use?

A) Amazon Rekognition Custom Labels
B) Amazon Personalize
C) Amazon Translate
D) Amazon Comprehend

Answer: A) Amazon Rekognition Custom Labels

Explanation:

Amazon Personalize handles recommendations. Amazon Translate performs language translation. Amazon Comprehend analyzes text. Amazon Rekognition Custom Labels allows training image classification models using custom datasets without deep learning expertise. This makes it suitable for pathology image classification.

Question 194

A global company wants to convert audio recordings of meetings into text for documentation. Which AWS service should they choose?

A) Amazon Transcribe
B) Amazon Polly
C) Amazon SageMaker
D) Amazon Comprehend

Answer: A) Amazon Transcribe

Explanation:

Amazon Polly turns text into speech. Amazon SageMaker can build custom models but is unnecessary for transcription. Amazon Comprehend analyzes text but cannot convert audio to text. Amazon Transcribe provides automated speech recognition and is designed specifically for transcribing audio. This makes it the suitable service.

Question 195

A company wants to automate answering common customer questions by retrieving information from FAQs and documents. Which AWS service is most appropriate?

A) Amazon Kendra
B) Amazon Translate
C) Amazon Lex
D) Amazon Macie

Answer: A) Amazon Kendra

Explanation:

Amazon Translate converts languages. Amazon Lex supports conversational interfaces but does not retrieve answers from documents by itself. Amazon Macie detects sensitive data in S3. Amazon Kendra indexes and searches enterprise documents using natural language and returns direct answers to queries. This makes it the best solution for automated FAQ retrieval.

Amazon AWS Certified AI Practitioner AIF-C0 Exam Dumps and Practice Test Questions Set 13 Q181-195

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