PCA practice questions 111–120

1. Question 111 · 335 in bank

   Your company plans to migrate a multi-petabyte data set to the cloud. The data set must be available 24hrs a day. Your business analysts have experience only with using a SQL interface. How should you store the data to optimize it for ease of analysis?

   • ALoad data into Google BigQuery
   • BInsert data into Google Cloud SQL
   • CPut flat files into Google Cloud Storage
   • DStream data into Google Cloud Datastore

   Show answer

   Answer: A

   Explanation

   This question could go either way for A or B. But Big Query was designed with this in mind, according to numerous Google presentation and videos. Cloud Datastore is a NoSQL database (https://cloud.google.com/datastore/docs/concepts/overview) Cloud Storage does not have an SQL interface. The previous two sentences eliminate options C and D. So I'd pick "A".

   Source: Adapted from community discussion — Eroc

   References

   • Google Cloud documentation
   • Google Cloud documentation

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2. Question 112 · 335 in bank

   You want to optimize the performance of an accurate, real-time, weather-charting application. The data comes from 50,000 sensors sending 10 readings a second, in the format of a timestamp and sensor reading. Where should you store the data?

   • AGoogle BigQuery
   • BGoogle Cloud SQL
   • CGoogle Cloud Bigtable
   • DGoogle Cloud Storage

   Show answer

   Answer: C

   Explanation

   New Question:2 (Part 1 of 2) You have a Python web application with many dependencies that requires 0.1 CPU cores and 128 MB of memory to operate in production. You want to monitor and maximize machine utilization. You also want to reliably deploy a new versions of the application. Which set of steps should you take? (A). Perform the following: 1. Create a managed instance group with f1-micro type machines. 2. Use a startup script to clone the repository. Check out the production branch, install the dependencies, and start the Python app. 3. Restart the instances to automatically deploy new production releases. (B). Perform the following: 1. Create a managed instance group with n 1-standard-1 type machines. 2. Build a Compute Engine image from the production branch that contains all the dependencies and automatically starts the Python app. 3. Rebuild the Compute Engine image, and update the instance template to deploy new production releases.

   Source: Adapted from community discussion — Umer24

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3. Question 113 · 335 in bank

   Your company has a Google Cloud project that uses BigOuery for data warehousing. The VPN tunnel between the on-premises environment and Google Cloud is configured with Cloud VPN. Your security team wants to avoid data exfiltration by malicious insiders, compromised code, and accidental oversharing. What should you do?

   • AConfigure Private Service Connect.
   • BConfigure VPC Service Controls and configure Private Google Access for on-promises hosts.
   • CCreate a service account, grant the BigQuery JobUser role and Storage Object Viewer role to the service account, and remove all other Identity and Access Management (IAM) access from the project.
   • DConfigure Private Google Access.

   Show answer

   Answer: B

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4. Question 114 · 335 in bank

   Your company's user-feedback portal comprises a standard LAMP stack replicated across two zones. It is deployed in the us-central1 region and uses autoscaled managed instance groups on all layers, except the database. Currently, only a small group of select customers have access to the portal. The portal meets a 99,99% availability SLA under these conditions. However next quarter, your company will be making the portal available to all users, including unauthenticated users. You need to develop a resiliency testing strategy to ensure the system maintains the SLA once they introduce additional user load. What should you do?

   • ACapture existing users input, and replay captured user load until autoscale is triggered on all layers. At the same time, terminate all resources in one of the zones
   • BCreate synthetic random user input, replay synthetic load until autoscale logic is triggered on at least one layer, and introduce ג€chaosג€ to the system by terminating random resources on both zones
   • CExpose the new system to a larger group of users, and increase group size each day until autoscale logic is triggered on all layers. At the same time, terminate random resources on both zones
   • DCapture existing users input, and replay captured user load until resource utilization crosses 80%. Also, derive estimated number of users based on existing user's usage of the app, and deploy enough resources to handle 200% of expected load

   Show answer

   Answer: B

   Explanation

   resilience test is not about load, is about terminate resources and service not affected. Think it's B. The best for resilience in to introduce chaos in the infraestructure

   Source: Adapted from community discussion — jcmoranp

   References

   • Google Cloud documentation
   • Google Cloud documentation

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5. Question 115 · 335 in bank

   Select 2

   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 two.)

   • ARemove Python after running pip
   • BRemove dependencies from requirements.txt
   • CUse a slimmed-down base image like Alpine Linux
   • DUse larger machine types for your Google Container Engine node pools
   • ECopy the source after he package dependencies (Python and pip) are installed

   Show answer

   Answer: CE

   Explanation

   C & E: C: Smaller the base image with minimum dependency faster the container will start E: Docker image build uses caching. Docker Instructions sequence matter because application’s dependencies change less frequently than the Python code which will help to reuse the cached layer of dependency and only add new layer for code change for Python Source code.

   Source: Adapted from community discussion — aviratna

   References

   • Google Cloud documentation

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6. Question 116 · 335 in bank

   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?

   • ADeploy fewer changes to production
   • BDeploy smaller changes to production
   • CIncrease the load on your test and staging environments
   • DDeploy changes to a small subset of users before rolling out to production

   Show answer

   Answer: C

   Explanation

   Question Statement: You want to adjust your test and deployment procedures to avoid this problem in the future So based on this, I think the option "C" is correct, since it is the only one talking about doing changes in the test environment.

   Source: Adapted from community discussion — ghitesh

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7. Question 117 · 335 in bank

   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?

   • ASet timeouts on your application so that you can fail requests faster
   • BSend custom metrics for each of your requests to Stackdriver Monitoring
   • CUse Stackdriver Monitoring to look for insights that show when your API latencies are high
   • DInstrument your application with Stackdriver Trace in order to break down the request latencies at each microservice

   Show answer

   Answer: D

   Explanation

   D. Instrument your application with Stackdriver Trace in order to break down the request latencies at each microservice Stackdriver Trace is a distributed tracing system that allows you to understand the relationships between requests and the various microservices that they touch as they pass through your application. By instrumenting your application with Stackdriver Trace, you can get a detailed breakdown of the latencies at each microservice, which can help you identify which service is taking the longest in those cases where a small number of API requests take a very long time. Setting timeouts on your application or sending custom metrics to Stackdriver Monitoring may not provide the level of detail that you need to identify the specific service that is causing the latency issues. Looking for insights in Stackdriver Monitoring may also not provide the necessary level of detail, as it may not show the individual latencies at each microservice.

   Source: Adapted from community discussion — omermahgoub

   References

   • Google Cloud documentation

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8. Question 118 · 335 in bank

   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?

   • AUse a different database
   • BChoose larger instances for your database
   • CCreate snapshots of your database more regularly
   • DImplement routinely scheduled failovers of your databases

   Show answer

   Answer: D

   Explanation

   @chiar, I agree the question i s not clear. In GCP larger instances have larger number of CPUs, Memory and come with their own private network. So increases the instance size would help prevent the need for failover during high traffic times. However, routinely scheduled failovers would allow the team to test the failover when it is not requried. This would make sure it is working when it is required.

   Source: Adapted from community discussion — Eroc

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9. Question 119 · 335 in bank

   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?

   • AGrant the security team access to the logs in each Project
   • BConfigure Stackdriver Monitoring for all Projects, and export to BigQuery
   • CConfigure Stackdriver Monitoring for all Projects with the default retention policies
   • DConfigure Stackdriver Monitoring for all Projects, and export to Google Cloud Storage

   Show answer

   Answer: D

   Explanation

   A and C can be quickly ruled out because none of them is solution for the requirements "retained for 5 years" Between B and D, the different is where to store, BigQuery or Cloud Storage. Since the main concern is extended storing period, D (Correct Answer) is better choice, and the "retained for 5 years for future analysis" further qualifies it, for example, using Coldline storage class. With regards of BigQuery, while it is also a low-cost storage, but the main purpose is for analysis. Also, logs stored in Cloud Storage is easy to transport to BigQuery or do query directly against the files saved in Cloud Storage if and whenever needed.

   Source: Adapted from community discussion — MeasService

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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10. Question 120 · 335 in bank

    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?

    • AGoogle Cloud Dedicated Interconnect
    • BGoogle Cloud VPN connected to the data center network
    • CA NAT and TLS translation gateway installed on-premises
    • DA Google Compute Engine instance with a VPN server installed connected to the data center network

    Show answer

    Answer: A

    Explanation

    Let's go with option elimination A. Google Cloud Dedicated Interconnect >> Secured, fast connection, hence the choice. This will allow private connection from GCP to the data centre with a fast connection. Cost is not mentioned in the requirement to eliminate this option. B. Google Cloud VPN connected to the data centre network >> We have to think about data flowing on the internet and the requirement talks about private connect. Also not sure how well you connect VPN with Data Center until you use the hybrid option. https://cloud.google.com/network-connectivity/docs/vpn/concepts/overview hence eliminate C. A NAT and TLS translation gateway installed on-premises >>This is a VM option to reach outside won't for this requirement hence eliminate D. A Google Compute Engine instance with a VPN server installed connected to the data centre network >>This is a slow option hence eliminate Hence A

    Source: Adapted from community discussion — amxexam

    References

    • Google Cloud documentation
    • Google Cloud documentation

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PCA practice, question 111. 335 questions available.