Professional-Cloud-Architect Practice Questions

A.  

Use Google App Engine with Google Cloud Endpoints. Focus on an API for dealers and partners.

B.  

Use Google App Engine with a JAX-RS Jersey Java-based framework. Focus on an API for the public.

C.  

Use Google App Engine with the Swagger (open API Specification) framework. Focus on an API for the public.

D.  

Use Google Container Engine with a Django Python container. Focus on an API for the public.

E.  

Use Google Container Engine with a Tomcat container with the Swagger (Open API Specification) framework. Focus on an API for dealers and partners.

A.  

Opex/capex allocation, LAN changes, capacity planning

B.  

Capacity planning, TCO calculations, opex/capex allocation

C.  

Capacity planning, utilization measurement, data center expansion

D.  

Data Center expansion, TCO calculations, utilization measurement

A.  

Create a Filestore Zonal instance in each zone where the VMs are running. Mount the Filestore file share on each Compute Engine VM.

B.  

Use a multi-region Cloud Storage bucket to store the files.

C.  

Create a Regional Persistent Disk and attach it to all VMs in multi-writer mode.

D.  

Create a Filestore Enterprise instance in the same region as the VMs. Mount the Filestore file share on each Compute Engine VM.

A.  

App Engine is the only compute platform on GCP that is certified for PCI DSS hosting.

B.  

Kubernetes Engine cannot be used under PCI DSS because it is considered shared hosting.

C.  

Kubernetes Engine and GCP provide the tools you need to build a PCI DSS-compliant environment.

D.  

All Google Cloud services are usable because Google Cloud Platform is certified PCI-compliant.

A.  

Replace the existing data warehouse with BigQuery. Use table partitioning.

B.  

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs.

C.  

Replace the existing data warehouse with BigQuery. Use federated data sources.

D.  

Replace the existing data warehouse with a Compute Engine instance with 96 CPUs. Add an additional Compute Engine pre-emptible instance with 32 CPUs.

A.  

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Standard”, and Action: “Set to Coldline”, and create a second GCS life-cycle rule with Age: “365”, Storage Class: “Coldline”, and Action: “Delete”.

B.  

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Coldline”, and Action: “Set to Nearline”, and create a second GCS life-cycle rule with Age: “91”, Storage Class: “Coldline”, and Action: “Set to Nearline”.

C.  

Create a Cloud Storage lifecycle rule with Age: “90”, Storage Class: “Standard”, and Action: “Set to Nearline”, and create a second GCS life-cycle rule with Age: “91”, Storage Class: “Nearline”, and Action: “Set to Coldline”.

D.  

Create a Cloud Storage lifecycle rule with Age: “30”, Storage Class: “Standard”, and Action: “Set to Coldline”, and create a second GCS life-cycle rule with Age: “365”, Storage Class: “Nearline”, and Action: “Delete”.

A.  

Deploy Cloud Run services to multiple availability zones. Create Cloud Endpoints that point to the services. Create a global HTIP(S) Load Balancing instance and attach the Cloud Endpoints to its backend.

B.  

Deploy Cloud Run services to multiple regions Create serverless network endpoint groups pointing to the services. Add the serverless NE Gs to a backend service that is used by a global HTIP(S) Load Balancing instance.

C.  

Cloud Run services to multiple regions. In Cloud DNS, create a latency-based DNS name that points to the services.

D.  

Deploy Cloud Run services to multiple availability zones. Create a TCP/IP global load balancer. Add the Cloud Run Endpoints to its backend service.

A.  

Use BigQuery as the data warehouse. Connect all vehicles to the network and stream data into BigQuery using Cloud Pub/Sub and Cloud Dataflow. Use Google Data Studio for analysis and reporting.

B.  

Use BigQuery as the data warehouse. Connect all vehicles to the network and upload gzip files to a Multi-Regional Cloud Storage bucket using gcloud. Use Google Data Studio for analysis and reporting.

C.  

Use Cloud Dataproc Hive as the data warehouse. Upload gzip files to a MultiRegional Cloud Storage

bucket. Upload this data into BigQuery using gcloud. Use Google data Studio for analysis and reporting.

D.  

Use Cloud Dataproc Hive as the data warehouse. Directly stream data into prtitioned Hive tables. Use Pig scripts to analyze data.

A.  

Request Transfer Appliances from Google Cloud, export the data to appliances, and return the appliances to Google Cloud.

B.  

Configure the Storage Transfer service from Google Cloud to send the data from your data center to Cloud Storage

C.  

Make sure there are no other users consuming the 1 Gbps link, and use multi-thread transfer to upload the data to Cloud Storage.

D.  

Export files to an encrypted USB device, send the device to Google Cloud, and request an import of the data to Cloud Storage

A.  

Create a token and pass it in as an environment variable to func_display. When invoking func_query, include the token in the request Pass the same token to func _query and reject the invocation if the tokens are different.

B.  

Make func_query ' Require authentication. ' Create a unique service account and associate it to func_display. Grant the service account invoker role for func_query. Create an id token in func_display and include the token to the request when invoking func_query.

C.  

Make func _query ' Require authentication ' and only accept internal traffic. Create those two functions in the same VP

C.  

Create an ingress firewall rule for func_query to only allow traffic from func_display.

D.  

Create those two functions in the same project and VP

C.  

Make func_query only accept internal traffic. Create an ingress firewall for func_query to only allow traffic from func_display. Also, make sure both functions use the same service account.