Your company has a project in Google Cloud with three Virtual Private Clouds (VPCs). There is a Compute Engine instance on each VPC. Network subnets do not overlap and must remain separated. The network configuration is shown below.

Instance #1 is an exception and must communicate directly with both Instance #2 and Instance #3 via internal IPs. How should you accomplish this?

• A.Add two additional NICs to Instance #1 with the following configuration:
  * NIC1
  * VPC: VPC #2
  * SUBNETWORK: subnet #2
  * NIC2
  * VPC: VPC #3
  * SUBNETWORK: subnet #3
  Update firewall rules to enable traffic between instances.
• B.Create a cloud router to advertise subnet #2 and subnet #3 to subnet #1.
• C.Create two VPN tunnels via CloudVPN:
  * 1 between VPC #1 and VPC #2.
  * 1 between VPC #2 and VPC #3.
  Update firewall rules to enable traffic between the instances.
• D.Peer all three VPCs:
  * Peer VPC #1 with VPC #2.
  * Peer VPC #2 with VPC #3.
  Update firewall rules to enable traffic between the instances.

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For this question, refer to the JencoMart case study.
The migration of JencoMart's application to Google Cloud Platform (GCP) is progressing too slowly. The infrastructure is shown in the diagram. You want to maximize throughput. What are three potential bottlenecks? (Choose 3 answers.)

• A.Fewer virtual machines (VMs) in GCP than on-premises machines
• B.A copy command that is not suited to operate over long distances
• C.A separate storage layer outside the VMs, which is not suited for this task
• D.A tier of Google Cloud Storage that is not suited for this task
• E.Complicated internet connectivity between the on-premises infrastructure and GCP
• F.A single VPN tunnel, which limits throughput

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Your company places a high value on being responsive and meeting customer needs quickly. Their primary business objectives are release speed and agility. You want to reduce the chance of security errors being accidentally introduced.
Which two actions can you take? Choose 2 answers.

• A.Ensure every code check-in is peer reviewed by a security SME
• B.Use source code security analyzers as part of the CI/CD pipeline
• C.Enable code signing and a trusted binary repository integrated with your CI/CD pipeline
• D.Run a vulnerability security scanner as part of your continuous-integration /continuous-delivery (CI/CD) pipeline
• E.Ensure you have stubs to unit test all interfaces between components

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You write a Python script to connect to Google BigQuery from a Google Compute Engine virtual machine.
The script is printing errors that it cannot connect to BigQuery.
What should you do to fix the script?

• A.Install the latest BigQuery API client library for Python
• B.Run your script on a new virtual machine with the BigQuery access scope enabled
• C.Create a new service account with BigQuery access and execute your script with that user
• D.Install the bq component for gcloud with the command gcloud components install bq.

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Your company has a Google Cloud project that uses BlgQuery for data warehousing There are some tables that contain personally identifiable information (PI!) Only the compliance team may access the PH. The other information in the tables must be available to the data science team. You want to minimize cost and the time it takes to assign appropriate access to the tables What should you do?

• A.1 From the dataset where you have the source data, create views of tables that you want to share, excluding Pll
  2 Assign an appropriate project-level IAM role to the members of the data science team
  3 Assign access controls to the dataset that contains the view
• B.1 From the dataset where you have the source data, create materialized views of tables that you want to share excluding Pll
  2 Assign an appropriate project-level IAM role to the members of the data science team 3. Assign access controls to the dataset that contains the view.
• C.1 Create a dataset for the data science team
  2 Create views of tables that you want to share excluding Pll
  3 Assign an appropriate project-level IAM role to the members of the data science team
  4 Assign access controls to the dataset that contains the view
  5 Authorize the view to access the source dataset
• D.1. Create a dataset for the data science team.
  2. Create materialized views of tables that you want to share, excluding Pll
  3. Assign an appropriate project-level IAM role to the members of the data science team
  4 Assign access controls to the dataset that contains the view
  5 Authorize the view to access the source dataset

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