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A research team has collected a large dataset of sensor readings from various industrial machines. This dataset includes measurements like temperature, pressure, vibration levels, and electrical current, recorded at regular intervals. The team has not yet assigned any labels or categories to these readings and wants to identify potential anomalies, malfunctions, or natural groupings of machine behavior based on the sensor data alone.
What type of machine learning should they use?
A. Unsupervised learning
B. Reinforcement learning
C. Supervised learning
D. Deep learning
Answer: A
Explanation:
Since the team has not yet assigned any labels or categories to the sensor readings and wants to identify
"anomalies, malfunctions, or natural groupings" based on the data alone, this is a classic unsupervised learning problem. Unsupervised learning techniques like clustering or anomaly detection are used to find hidden patterns or structures in unlabeled data.
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NEW QUESTION # 20
According to Google-recommended practices, when should generative AI be used to automate tasks?
A. When tasks are complex and require strategic decision-making.
B. When tasks are repetitive and rule-based.
C. When tasks are highly creative and require original thought.
D. When tasks involve sensitive information or require human oversight
Answer: B
Explanation:
The strategic value of Generative AI (Gen AI) in a business context, as taught in Google's courses, is primarily to enhance efficiency and productivity by taking over tasks that consume significant employee time.
Gen AI excels in automating tasks that:
Are repetitive and time-consuming, such as drafting initial emails, summarizing long documents, or generating code snippets. Automating these routine tasks (C) frees employees to focus on higher-value activities (like building customer relationships or strategic planning).
Involve the generation of new content based on patterns learned from large datasets (e.g., text, images, code).
Options A and D represent high-value, strategic work-highly creative or complex strategic decision-making-where human judgment and oversight remain paramount. While Gen AI can assist with these (e.g., brainstorming creative ideas or providing data-backed insights), it is generally not recommended for full automation. Option B explicitly requires human oversight due to its sensitive nature. Therefore, the best fit for full or augmented automation for efficiency is the handling of routine, repeatable, and non-complex tasks.
(Reference: Google Cloud documentation on Gen AI adoption and efficiency states that Gen AI transforms work by automating repetitive and time-consuming tasks to free up time for strategic thinking and creativity.)
NEW QUESTION # 21
A learning and development team wants to quickly create a new hire training video with a custom avatar and voiceover that matches their company's branding and key messaging. They did not receive any money to spend on the production. What should they do?
A. Generate the video frames with Imagen.
B. Train a model with Vertex AI and produce a video.
C. Create a video with Google Vids.
D. Prompt the Gemini app to create a video.
Answer: C
Explanation:
The scenario requires quick creation of a training video using a custom avatar and voiceover while adhering to zero cost for production.
Google Vids is an AI-powered video creation app (part of Google Workspace/Gemini features) designed to make video creation accessible for teams without the overhead of traditional production. It specifically offers features like AI avatars and voiceovers for content such as trainings, demos, and onboarding videos. This directly addresses the need for a low-cost, fast solution for a new hire training video with custom branding elements (custom avatars and voiceovers are a key feature of the tool).
Option A, Imagen, is a Google foundation model specialized for image generation, not the creation of structured, narrated training videos with avatars. Option B, using the Gemini app, is primarily for text, code, and multimodal chat/generation, and is not the dedicated Google application for video production. Option C, training a model with Vertex AI, is a highly technical, time-consuming, and expensive endeavor that violates the need for a quick and zero-cost solution. Therefore, using the purpose-built, gen AI-enabled Google Vids application is the correct and most efficient choice.
NEW QUESTION # 22
A user asks a generative AI model about the scientific accuracy of a popular science fiction movie. The model confidently states that humans can indeed travel faster than light, referencing specific but entirely fictional theories and providing made-up explanations of how this is achieved according to the movie's "established science." The model presents this information as factual, without indicating that it originates from a fictional work. What type of model limitation is this?
A. Bias
B. Data dependency
C. Knowledge cutoff
D. Hallucination
Answer: D
Explanation:
The limitation described is the AI model generating a false or misleading response (humans traveling faster than light is scientifically impossible/unproven) and presenting it as fact (confidently stating a fictional theory is real) without the ability to indicate its uncertainty or the source's fictional nature. This is the definition of a Hallucination in generative AI.
AI Hallucinations occur when a Large Language Model (LLM) generates outputs that are factually incorrect, irrelevant, or nonsensical, despite being linguistically fluent and seemingly plausible. They arise because the model is designed to predict the most statistically probable next word or token based on its training data, even when it lacks information or when its training data contains a mixture of fact and fiction. The model is overconfident in its generated response, a behavior that diminishes user trust and reliability, especially in applications where factual accuracy is critical. While a knowledge cutoff (B) is a common cause of hallucinations when an LLM is asked about recent events, the core limitation of fabricating facts from its own hardwired knowledge is the hallucination itself. Data dependency (A) relates to the model's reliance on the quality and completeness of its training data, and while flawed training data can be a cause, the error mode of inventing facts is the Hallucination.
NEW QUESTION # 23
A company trains a generative AI model designed to classify customer feedback as positive, negative, or neutral. However, the training dataset disproportionately includes feedback from a specific demographic and uses outdated language norms that don't reflect current customer communication styles. When the model is deployed, it shows a strong bias in its sentiment analysis for new customer feedback, misclassifying reviews from underrepresented demographics and struggling to understand current slang or phrasing. What type of model limitation is this?
A. Data dependency
B. Overfitting
C. Edge case
D. Hallucination
Answer: A
Explanation:
The core reason for the model's failure is that the training data itself was flawed (disproportionate demographic representation and outdated language). This flaw directly leads to the observed bias and poor performance on underrepresented groups and modern communication styles.
This is a classic example of Data Dependency, a fundamental limitation of all machine learning models, including generative AI. Data dependency refers to the absolute reliance of an AI model on the quality, completeness, and fairness of the data on which it was trained. Since the model essentially only mimics the patterns it learned from its dataset, if the dataset contains societal, demographic, or linguistic biases, the model will faithfully reproduce and amplify those biases in its output, leading to unfair classification for certain groups.
Hallucination (C) is the invention of facts or data.
Overfitting (D) is poor generalization because the model memorized the training data too well, typically resulting in very poor performance across all unseen data, not just specific demographics.
Bias is the result of the data dependency, not the fundamental limitation itself.
(Reference: Google's training on Generative AI Limitations identifies Data Dependency as the fundamental limitation where the model is limited by the scope and quality of its training data, directly leading to issues of bias when the data is not diverse or representative.)
NEW QUESTION # 24
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