An organization has years of archived call-center recordings and sensor log files it has never analyzed. Which statement best explains why moving these datasets to Google Cloud can help the company unlock new business value from them?
Any file copied to the cloud is automatically transformed into structured database rows, removing the need for separate analytics tools.
Google Cloud supplies virtually limitless, cost-tiered storage plus managed analytics and AI services, letting the company extract insights from large unstructured datasets without investing in new infrastructure.
Using a cloud platform eliminates the need for data governance controls, accelerating experimentation with sensitive information.
Cloud migration guarantees duplicate records are eliminated, shrinking data volume by 90 % and immediately revealing hidden patterns.
Google Cloud offers object storage that can scale almost without limit at different price tiers, making it cost-effective to land large quantities of raw, unstructured data. Once in the cloud, the data can be processed with fully managed analytics (BigQuery, Dataflow) or AI services (Speech-to-Text, Vision, AutoML) without the company purchasing or maintaining on-premises hardware. These capabilities turn previously untapped files such as audio or log data into insights, whereas the distractors either describe features the cloud does not provide (automatic conversion to rows, guaranteed deduplication) or promote poor governance practices that would hinder, not help, value creation.
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What is unstructured data and how does Google Cloud handle it?
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How do AI services like Speech-to-Text and Vision in Google Cloud assist in analyzing data?
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What is Google BigQuery and how does it enable analytics for large datasets?
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What is unstructured data?
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What is Google Cloud’s BigQuery and how does it help with analytics?
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What are Google Cloud’s AI tools and how can they extract insights from unstructured data?
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