You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

• A.Grant the consultant the Viewer role on the project.
• B.Grant the consultant the Cloud Dataflow Developer role on the project.
• C.Create a service account and allow the consultant to log on with it.
• D.Create an anonymized sample of the data for the consultant to work with in a different project.

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You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• A.Deploy a Cloud Dataproc cluster. Use an SSD persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• B.Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.
  Store data in HDFS. Change references in scripts from hdfs:// to gs://
• C.Deploy a Cloud Dataproc cluster. Use a standard persistent disk and 50% preemptible workers. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• D.Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instances. Install the Cloud Storage connector, and store the data in Cloud Storage. Change references in scripts from hdfs:// to gs://

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You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

• A.Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
• B.Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
• C.Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
• D.Add capacity (memory and disk space) to the database server by the order of 200.

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You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

• A.Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
• B.Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.
• C.Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
• D.Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.

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What are two methods that can be used to denormalize tables in BigQuery?

• A.1) Split table into multiple tables; 2) Use a partitioned table
• B.1) Join tables into one table; 2) Use nested repeated fields
• C.1) Use a partitioned table; 2) Join tables into one table
• D.1) Use nested repeated fields; 2) Use a partitioned table

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