material de estudo XXII

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9) The implementation of artificial intelligence in the field of finance has led to the 
development of algorithmic trading systems that can execute trades at high speeds based 
on predefined strategies. However, these systems also pose significant risks to market 
stability. Which of the following best illustrates a potential risk associated with algorithmic 
trading in financial markets, particularly during periods of high volatility? 
A) Algorithmic trading systems operate independently of market conditions, ensuring 
consistent performance regardless of volatility. 
B) Algorithmic trading can contribute to flash crashes, where rapid selling by automated 
systems leads to drastic price declines and market instability. 
C) Algorithmic trading always results in positive returns for investors, regardless of market 
conditions. 
D) The use of algorithmic trading eliminates the need for human oversight and regulatory 
compliance, as the algorithms are infallible. 
E) Algorithmic trading systems are designed to slow down trading during volatile periods to 
protect market integrity. 
Correct option: B) 
Explanation: Algorithmic trading can contribute to flash crashes, where rapid automated 
selling leads to drastic price declines and market instability, highlighting the risks 
associated with high-speed trading during volatile conditions. 
 
10) In the context of deep learning, the technique of transfer learning has emerged as a 
valuable approach for improving model performance, particularly when training data is 
limited. Which of the following statements best defines transfer learning and its 
significance in the development of deep learning models? 
A) Transfer learning involves training a model from scratch on a large dataset without 
leveraging any pre-existing knowledge. 
B) Transfer learning allows a model trained on one task to be fine-tuned for a related task, 
thus reducing the need for extensive labeled data and improving performance. 
C) Transfer learning is only applicable to supervised learning tasks and has no relevance in 
unsupervised learning contexts. 
D) Transfer learning relies solely on the original dataset without any modifications or 
adjustments to improve performance on new tasks. 
E) Transfer learning is a technique used to create entirely new models without relying on 
any prior knowledge or existing architectures. 
Correct option: B) 
Explanation: Transfer learning allows a model trained on one task to be fine-tuned for a 
related task, significantly reducing the need for extensive labeled data and improving 
performance in scenarios where training data is limited. 
 
11) The advent of AI-driven chatbots has transformed customer service in various 
industries, enabling businesses to provide 24/7 support. However, the effectiveness of 
these chatbots often depends on their ability to understand and respond to user queries 
accurately. Which of the following factors is most crucial in enhancing the natural 
language understanding capabilities of AI chatbots to ensure satisfactory customer 
interactions? 
A) Utilizing a fixed set of scripted responses to address customer inquiries without room 
for variation. 
B) Employing machine learning algorithms that are trained on diverse datasets, enabling 
the chatbot to comprehend a broad range of language constructs and user intents. 
C) Limiting the chatbot's knowledge base to a narrow set of topics to avoid confusing 
users. 
D) Designing chatbots to operate solely based on keywords, disregarding the context of 
the conversation. 
E) Ensuring that chatbots can only respond to simple yes-or-no questions to maintain 
simplicity. 
Correct option: B) 
Explanation: Enhancing the natural language understanding capabilities of AI chatbots 
relies on machine learning algorithms trained on diverse datasets, allowing them to 
comprehend a wide range of language constructs and user intents. 
 
12) The integration of machine learning algorithms in predictive maintenance has 
significantly improved the efficiency of industrial operations. By analyzing equipment data, 
these algorithms can forecast potential failures and schedule maintenance proactively. 
Which of the following statements best captures the role of machine learning in predictive 
maintenance and its impact on operational efficiency? 
A) Predictive maintenance solely relies on historical failure rates without considering real-
time data from equipment sensors. 
B) Machine learning algorithms analyze real-time data from equipment sensors to identify 
patterns and anomalies, enabling timely interventions that reduce downtime and 
maintenance costs. 
C) Predictive maintenance is only beneficial for large industrial operations and has no 
applicability in smaller businesses. 
D) Machine learning models in predictive maintenance are limited to binary classification 
tasks, making them unsuitable for complex failure predictions. 
E) Predictive maintenance does not require any data analysis, as maintenance schedules 
can be determined based on manufacturer recommendations alone. 
Correct option: B) 
Explanation: Machine learning algorithms in predictive maintenance analyze real-time 
data from equipment sensors to identify patterns and anomalies, allowing for timely 
interventions that reduce downtime and maintenance costs. 
 
13) The implementation of AI technologies in the recruitment process has raised concerns 
about the potential for bias in hiring decisions. Which of the following scenarios best 
illustrates how bias can manifest in AI-driven recruitment systems, particularly in relation 
to historical data? 
A) An AI system trained on a diverse dataset is less likely to exhibit bias in its hiring 
recommendations. 
B) An AI recruitment tool that learns from historical hiring data may inadvertently 
perpetuate existing biases by favoring candidates similar to those previously hired, 
thereby disadvantaging underrepresented groups. 
C) AI recruitment systems are programmed to ignore demographic information, ensuring 
that all candidates are evaluated equally. 
D) The inherent objectivity of AI systems guarantees that hiring decisions will always be 
fair and unbiased. 
E) AI-driven recruitment tools are designed to prioritize qualifications over any historical 
data, eliminating bias from the selection process. 
Correct option: B) 
Explanation: AI recruitment systems trained on historical hiring data may inadvertently 
perpetuate existing biases by favoring candidates similar to those previously hired, 
disadvantaging underrepresented groups and highlighting the need for careful 
consideration of data sources. 
 
14) In the field of computer vision, convolutional neural networks (CNNs) have become a 
fundamental architecture for processing visual data. Which of the following statements 
best describes the key feature of CNNs that enables them to excel in image recognition 
tasks? 
A) CNNs utilize fully connected layers only, disregarding spatial hierarchies in images. 
B) The convolutional layers in CNNs are designed to automatically learn spatial 
hierarchies and patterns within images, allowing for effective feature extraction. 
C) CNNs rely exclusively on traditional machine learning techniques without any deep 
learning components. 
D) The performance of CNNs is limited to grayscale images and does not extend to color 
images. 
E) CNNs are primarily used for text processing and are not applicable to image recognition 
tasks. 
Correct option: B)