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Google Professional Data Engineer

Google Professional Data Engineer

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MJTelco is a startup focused on developing networks in rapidly growing and underserved global markets. They aim to establish a distributed data infrastructure capable of driving real-time analysis and incorporating machine learning to continuously optimize their network topologies. Utilizing inexpensive hardware, MJTelco plans to over-deploy their network and operate across three distinct environments: development/test, staging, and production.

MJTelco has specified the following requirements for a custom interface to facilitate data sharing:

  1. Perform aggregations on petabyte-scale datasets.
  2. Execute scans over specific time range rows with very fast response times (within milliseconds).

Given these requirements, which combination of Google Cloud Platform products would you recommend?

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Explanation:

The correct answer is C: BigQuery and Cloud Bigtable. BigQuery is suitable for performing aggregations over petabyte-scale datasets due to its powerful analytics capabilities, while Cloud Bigtable is ideal for scanning specific time-range rows with very fast response times (milliseconds). Together, these products meet MJTelco's requirements for data aggregation and fast time-range scans.

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