Verified Professional-Cloud-Architect Dumps | 2019 Professional-Cloud-Architect PDF Question & Answers
                      
 
 
 
 
 
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Mix Questions 
 
Question: 1 
 
One of the developers on your team deployed their application in Google Container Engine with the 
Dockerfile below. They report that their application deployments are taking too long. 
 
You want to optimize this Dockerfile for faster deployment times without adversely affecting the app’s 
functionality. 
Which two actions should you take? Choose 2 answers. 
 
A. Remove Python after running pip. 
B. Remove dependencies from requirements.txt. 
C. Use a slimmed-down base image like Alpine linux. 
D. Use larger machine types for your Google Container Engine node pools. 
E. Copy the source after the package dependencies (Python and pip) are installed. 
 
Answer: C,E 
 
Explanation: 
The speed of deployment can be changed by limiting the size of the uploaded app, limiting the 
complexity of the build necessary in the Dockerfile, if present, and by ensuring a fast and reliable 
internet connection. 
Note: Alpine Linux is built around musl libc and busybox. This makes it smaller and more resource 
efficient than traditional GNU/Linux distributions. A container requires no more than 8 MB and a 
minimal installation to disk requires around 130 MB of storage. Not only do you get a fully-fledged Linux 
environment but a large selection of packages from the repository. 
References: https://groups.google.com/forum/#!topic/google-appengine/hZMEkmmObDU 
https://www.alpinelinux.org/about/ 
 
Question: 2 
 
Your solution is producing performance bugs in production that you did not see in staging and test 
environments. You want to adjust your test and deployment procedures to avoid this problem in the 
future. What should you do? 
 
A. Deploy fewer changes to production. 
B. Deploy smaller changes to production. 
 
C. Increase the load on your test and staging environments. 
D. Deploy changes to a small subset of users before rolling out to production. 
 
Answer: D 
 
Question: 3 
 
Your company has decided to make a major revision of their API in order to create better experiences 
for their developers. They need to keep the old version of the API available and deployable, while 
allowing new customers and testers to try out the new API. They want to keep the same SSL and DNS 
records in place to serve both APIs. What should they do? 
 
A. Configure a new load balancer for the new version of the API. 
B. Reconfigure old clients to use a new endpoint for the new API. 
C. Have the old API forward traffic to the new API based on the path. 
D. Use separate backend pools for each API path behind the load balancer. 
 
Answer: D 
 
https://cloud.google.com/endpoints/docs/openapi/lifecycle-management 
 
Question: 4 
 
A small number of API requests to your microservices-based application take a very long time. You know 
that each request to the API can traverse many services. You want to know which service takes the 
longest in those cases. What should you do? 
 
A. Set timeouts on your application so that you can fail requests faster. 
B. Send custom metrics for each of your requests to Stackdriver Monitoring. 
C. Use Stackdriver Monitoring to look for insights that show when your API latencies are high. 
D. Instrument your application with Stackdnver Trace in order to break down the request latencies at 
each microservice. 
 
Answer: D 
 
https://cloud.google.com/trace/docs/overview 
 
Question: 5 
 
During a high traffic portion of the day, one of your relational databases crashes, but the replica is never 
promoted to a master. You want to avoid this in the future. What should you do? 
 
 
A. Use a different database. 
B. Choose larger instances for your database. 
C. Create snapshots of your database more regularly. 
D. Implement routinely scheduled failovers of your databases. 
 
Answer: C 
 
Explanation: 
Take regular snapshots of your database system. 
If your database system lives on a Compute Engine persistent disk, you can take snapshots of your 
system each time you upgrade. If your database system goes down or you need to roll back to a 
previous version, you can simply create a new persistent disk from your desired snapshot and make that 
disk the boot disk for a new Compute Engine instance. Note that, to avoid data corruption, this 
approach requires you to freeze the database system's disk while taking a snapshot. 
Reference: https://cloud.google.com/solutions/disaster-recovery-cookbook 
 
Question: 6 
 
Your organization requires that metrics from all applications be retained for 5 years for future analysis in 
possible legal proceedings. Which approach should you use? 
 
A. Grant the security team access to the logs in each Project. 
B. Configure Stackdriver Monitoring for all Projects, and export to BigQuery. 
C. Configure Stackdriver Monitoring for all Projects with the default retention policies. 
D. Configure Stackdriver Monitoring for all Projects, and export to Google Cloud Storage. 
 
Answer: B 
 
https://cloud.google.com/monitoring/api/v3/metrics 
Explanation: 
Stackdriver Logging provides you with the ability to filter, search, and view logs from your cloud and 
open source application services. Allows you to define metrics based on log contents that are 
incorporated into dashboards and alerts. Enables you to export logs to BigQuery, Google Cloud Storage, 
and Pub/Sub. 
References: https://cloud.google.com/stackdriver/ 
 
Question: 7 
 
Your company has decided to build a backup replica of their on-premises user authentication 
PostgreSQL database on Google Cloud Platform. The database is 4 TB, and large updates are frequent. 
Replication requires private address space communication. Which networking approach should you use? 
 
A. Google Cloud Dedicated Interconnect 
B. Google Cloud VPN connected to the data center network 
 
C. A NAT and TLS translation gateway installed on-premises 
D. A Google Compute Engine instance with a VPN server installed connected to the data center network 
 
Answer: A 
 
https://cloud.google.com/docs/enterprise/best-practices-for-enterprise-organizations 
Explanation: 
Google Cloud Dedicated Interconnect provides direct physical connections and RFC 1918 communication 
between your on-premises network and Google’s network. Dedicated Interconnect enables you to 
transfer large amounts of data between networks, which can be more cost effective than purchasing 
additional bandwidth over the public Internet or using VPN tunnels. 
Benefits: 
Traffic between your on-premises network and your VPC network doesn't traverse the public Internet. 
Traffic traverses a dedicated connection with fewer hops, meaning there are less points of failure where 
traffic might get dropped or disrupted. 
Your VPC network's internal (RFC 1918) IP addresses are directly accessible from your on-premises 
network. You don't need to use a NAT device or VPN tunnel to reach internal IP addresses. Currently, 
you can only reach internal IP addresses over a dedicated connection. To reach Google external IP 
addresses, you must use a separate connection. 
You can scale your connection to Google based on your needs. Connection capacity is delivered over 
one or more 10 Gbps Ethernet connections, with a maximum of eight connections (80 Gbps total per 
interconnect). 
The cost of egress traffic from your VPC network to your on-premises network is reduced. A dedicated 
connection is generally the least expensive method if you have a high-volume of traffic to and from 
Google’s network. 
References: https://cloud.google.com/interconnect/docs/details/dedicated 
 
Question: 8 
 
Your company is forecasting a sharp increase in the number and size of Apache Spark and Hadoop jobs 
being run on your local datacenter You want to utilize the cloud to help you scale this upcoming demand 
with the least amount of operations work and code change. Which product should you use? 
 
A. Google Cloud Dataflow 
B. Google Cloud Dataproc 
C. Google Compute Engine 
D. Google Container Engine 
 
Answer: B 
 
Explanation: 
Google Cloud Dataproc is a fast, easy-to-use, low-cost and fully managed service that lets you run the 
Apache Spark and Apache Hadoop ecosystem on Google Cloud Platform. Cloud Dataproc provisions big 
or small clusters rapidly, supports many popular job types, and is integrated with other Google Cloud 
Platform services, such as Google Cloud Storage and Stackdriver Logging, thus helping you reduce TCO. 
 
References: https://cloud.google.com/dataproc/docs/resources/faq 
 
Question: 9 
 
Your company's test suite is a custom C++ application that runs tests throughout each day on Linux 
virtual machines. The full test suite takes several hours to complete, running on a limited number of on 
premises servers reserved for testing. Your company wants to move the testing infrastructure to the 
cloud, to reduce the amount of time it takes to fully test a change to the system, while changing the 
tests as little as possible. Which cloud infrastructure should you recommend? 
 
A. Google Compute Engine unmanaged instance groups and Network Load Balancer 
B. Google Compute Engine managed instance groups with auto-scaling 
C. Google Cloud Dataproc to run Apache Hadoop jobs to process each test 
D. Google App Engine with Google Stackdriver for logging 
 
Answer: B 
 
https://cloud.google.com/compute/docs/instance-groups/ 
Google Compute Engine enables users to launch virtual machines (VMs) on demand. VMs can be 
launched from the standard images or custom images created by users. 
Managed instance groups offer autoscaling capabilities that allow you to automatically add or remove 
instances from a managed instance group based on increases or decreases in load. Autoscaling helps 
your applications gracefully handle increases in traffic and reduces cost when the need for resources is 
lower. 
 
Question: 10 
 
Auditors visit your teams every 12 months and ask to review all the Google Cloud Identity and Access 
Management (Cloud IAM) policy changes in the previous 12 months. You want to streamline and 
expedite the analysis and audit process. What should you do? 
 
A. Create custom Google Stackdriver alerts and send them to the auditor. 
B. Enable Logging export to Google BigQuery and use ACLs and views to scope the data shared with the 
auditor. 
C. Use cloud functions to transfer log entries to Google Cloud SQL and use ACLS and views to limit an 
auditor's view. 
D. Enable Google Cloud Storage (GCS) log export to audit logs Into a GCS bucket and delegate access to 
the bucket. 
 
Answer: D 
 
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