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Google ASSOCIATE-DATA-PRACTITIONER

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Exam contains 106 questions

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Question 97 🔥

Your organization consists of two hundred employees on five different teams. The leadership team is concerned that any employee can move or delete all Looker dashboards saved in the Shared folder. You need to create an easy -to-manage solution that allows the five different teams in your organization to view content in the Shared folder, but only be able to move or delete their team - specific dashboard. What should you do?

Question 98 🔥

Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

Question 99 🔥

Your organization's website uses an on -premises MySQL as a backend database. You need to migrate the on -premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

Question 100 🔥

Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?

Question 101 🔥

Your company has an on -premises file server with 5 TB of data that needs to be migrated to Google Cloud. The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

Question 102 🔥

You work for a gaming company that collects real-time player activity dat a. This data is streamed into Pub/Sub and needs to be processed and loaded into BigQuery for analysis. The processing involves filtering, enriching, and aggregating the data before loading it into partitioned BigQuery tables. You need to design a pipeline that ensures low latency and high throughput while following a Google -recommended approach. What should you do?

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