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Professional Cloud Developer test Dumps

Google-PCD test Format | Course Contents | Course Outline | test Syllabus | test Objectives

A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.



The Professional Cloud Developer test assesses your ability to:

Design highly scalable, available, and reliable cloud-native applications

Build and test applications

Deploy applications

Integrate Google Cloud Platform services

Manage application performance monitoring



A Professional Cloud Developer builds scalable and highly available applications using Google-recommended practices and tools that leverage fully managed services. This individual has experience with cloud-native applications, runtime environments, developer tools, and next-generation databases. A Professional Cloud Developer also has proficiency with at least one general-purpose programming language and is skilled at producing meaningful metrics and logs to debug and trace code.



Section 1: Designing highly scalable, available, and reliable cloud-native applications

1.1 Designing high-performing applications and APIs. Considerations include:

- Microservices

- Scaling velocity characteristics/tradeoffs of IaaS (infrastructure as a service) vs. CaaS (container as a service) vs. PaaS (platform as a service)

- Evaluating different services and technologies

- Geographic distribution of Google Cloud services (e.g., latency, regional services, zonal services)

- Defining a key structure for high-write applications using Cloud Storage, Cloud Bigtable, Cloud Spanner, or Cloud SQL

- User session management

- Caching solutions

- Deploying and securing API services

- Loosely coupled applications using asynchronous Cloud Pub/Sub events

- Graceful shutdown on platform termination

- Google-recommended practices and documentation



1.2 Designing secure applications. Considerations include:

- Implementing requirements that are relevant for applicable regulations (e.g., data wipeout)

- Security mechanisms that protect services and resources

- Security mechanisms that secure/scan application binaries and manifests

- Storing and rotating application secrets using Cloud KMS

- Authenticating to Google services (e.g., application default credentials, JWT, OAuth 2.0)

- IAM roles for users/groups/service accounts

- Securing service-to-service communications (e.g., service mesh, Kubernetes network policies, and Kubernetes namespaces)

- Set compute/workload identity to least privileged access

- Certificate-based authentication (e.g., SSL, mTLS)

- Google-recommended practices and documentation



1.3 Managing application data. Tasks include:

- Defining database schemas for Google-managed databases (e.g., Cloud Firestore, Cloud Spanner, Cloud Bigtable, Cloud SQL)

- Choosing data storage options based on use case considerations, such as:

- Cloud Storage-signed URLs for user-uploaded content

- Structured vs. unstructured data

- Strong vs. eventual consistency

- Data volume

- Frequency of data access in Cloud Storage

- Following Google-recommended practices and documentation



1.4 Refactoring applications to migrate to Google Cloud. Tasks include:

- Using managed services

- Migrating a monolith to microservices

- Google-recommended practices and documentation



Section 2: Building and Testing Applications

2.1 Setting up your local development environment. Considerations include:

- Emulating Google Cloud services for local application development

- Creating Google Cloud projects



2.2 Writing code. Considerations include:

- Algorithm design

- Modern application patterns

- Efficiency

- Agile software development

- Unit testing



2.3 Testing. Considerations include:

- Performance testing

- Integration testing

- Load testing



2.4 Building. Considerations include:

- Creating a Cloud Source Repository and committing code to it

- Creating container images from code

- Developing a continuous integration pipeline using services (e.g., Cloud Build, Container Registry) that construct deployment artifacts

- Reviewing and improving continuous integration pipeline efficacy



Section 3: Deploying applications

3.1 Recommend appropriate deployment strategies for the target compute environment (Compute Engine, Google Kubernetes Engine). Strategies include:

- Blue/green deployments

- Traffic-splitting deployments

- Rolling deployments

- Canary deployments



3.2 Deploying applications and services on Compute Engine. Tasks include:

- Installing an application into a VM

- Modifying the VM service account

- Manually updating dependencies on a VM

- Exporting application logs and metrics

- Managing Compute Engine VM images and binaries



3.3 Deploying applications and services to Google Kubernetes Engine (GKE). Tasks include:

- Deploying a containerized application to GKE

- Managing Kubernetes RBAC and Google Cloud IAM relationship

- Configuring Kubernetes namespaces and access control

- Defining workload specifications (e.g., resource requirements)

- Building a container image using Cloud Build

- Configuring application accessibility to user traffic and other services

- Managing container lifecycle

- Define deployments, services, and pod configurations


3.4 Deploying a Cloud Function. Types include:

- Cloud Functions that are triggered via an event (e.g., Cloud Pub/Sub events, Cloud Storage object change notification events)

- Cloud Functions that are invoked via HTTP

- Securing Cloud Functions



3.5 Using service accounts. Tasks include:

- Creating a service account according to the principle of least privilege

- Downloading and using a service account private key file



Section 4: Integrating Google Cloud Platform Services

4.1 Integrating an application with data and storage services. Tasks include:

- Read/write data to/from various databases (e.g., SQL, JDBC)

- Connecting to a data store (e.g., Cloud SQL, Cloud Spanner, Cloud Firestore, Cloud Bigtable)

- Writing an application that publishes/consumes data asynchronously (e.g., from Cloud Pub/Sub)

- Storing and retrieving objects from Cloud Storage

- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools



4.2 Integrating an application with compute services. Tasks include:

- Implementing service discovery in Google Kubernetes Engine and Compute Engine

- memorizing instance metadata to obtain application configuration

- Authenticating users by using OAuth2.0 Web Flow and Identity Aware Proxy

- Using the command-line interface (CLI), Google Cloud Console, and Cloud Shell tools



4.3 Integrating Google Cloud APIs with applications. Tasks include:

- Enabling a Google Cloud API

- Making API calls with a Cloud Client Library, the REST API, or the APIs Explorer, taking into consideration:

- Batching requests

- Restricting return data

- Paginating results

- Caching results

- Error handling (e.g., exponential backoff)

- Using service accounts to make Google API calls



Section 5: Managing Application Performance Monitoring

5.1 Managing Compute Engine VMs. Tasks include:

- Debugging a custom VM image using the serial port

- Analyzing a failed Compute Engine VM startup

- Analyzing logs

- Sending logs from a VM to Cloud Monitoring

- Inspecting resource utilization over time

- Viewing syslogs from a VM



5.2 Managing Google Kubernetes Engine workloads. Tasks include:

- Configuring logging and monitoring

- Analyzing container lifecycle events (e.g., CrashLoopBackOff, ImagePullErr)

- Analyzing logs

- Using external metrics and corresponding alerts

- Configuring workload autoscaling



5.3 Troubleshooting application performance. Tasks include:

- Creating a monitoring dashboard

- Writing custom metrics and creating metrics from logs

- Graphing metrics

- Using Cloud Debugger

- Reviewing stack traces for error analysis

- Exporting logs from Google Cloud

- Viewing logs in the Google Cloud Console

- Profiling performance of request-response

- Profiling services

- Reviewing application performance (e.g., Cloud Trace, Prometheus, OpenCensus)

- Monitoring and profiling a running applicationv
- Using documentation, forums, and Google support

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Question: 39
Your teammate has asked you to review the code below. Its purpose is to efficiently add a large number of small rows to a BigQuery table.
Which improvement should you suggest your teammate make?
A. Include multiple rows with each request.
B. Perform the inserts in parallel by creating multiple threads.
C. Write each row to a Cloud Storage object, then load into BigQuery.
D. Write each row to a Cloud Storage object in parallel, then load into BigQuery.
Answer: B
Question: 40
You need to migrate an internal file upload API with an enforced 500-MB file size limit to App Engine.
What should you do?
A. Use FTP to upload files.
B. Use CPanel to upload files.
C. Use signed URLs to upload files.
D. Change the API to be a multipart file upload API.
Answer: C
Reference: https://wiki.christophchamp.com/index.php?title=Google_Cloud_Platform
Question: 41
Your company wants to expand their users outside the United States for their popular application. The company wants to ensure 99.999% availability of the
database for their application and also wants to minimize the read latency for their users across the globe.
Which two actions should they take? (Choose two.)
A. Create a multi-regional Cloud Spanner instance with "nam-asia-eur1" configuration.
B. Create a multi-regional Cloud Spanner instance with "nam3" configuration.
C. Create a cluster with at least 3 Spanner nodes.
D. Create a cluster with at least 1 Spanner node.
E. Create a minimum of two Cloud Spanner instances in separate regions with at least one node.
F. Create a Cloud Dataflow pipeline to replicate data across different databases.
Answer: BF
Question: 42
You have an application deployed in production. When a new version is deployed, some issues dont arise until the application receives traffic from users in
production. You want to reduce both the impact and the number of users affected.
Which deployment strategy should you use?
A. Blue/green deployment
B. Canary deployment
C. Rolling deployment
D. Recreate deployment
Answer: A
Reference: https://thenewstack.io/deployment-strategies/
Question: 43
You need to copy directory local-scripts and all of its contents from your local workstation to a Compute Engine virtual machine instance.
Which command should you use?
A. gsutil cp project my-gcp-project -r ~/local-scripts/ gcp-instance-name:~/server-scripts/ zone us-east1-b
B. gsutil cp project my-gcp-project -R ~/local-scripts/ gcp-instance-name:~/server-scripts/ zone us-east1-b
C. gcloud compute scp project my-gcp-project recurse ~/local-scripts/ gcp-instance-name:~/server-scripts/ zone us-east1-b
D. gcloud compute mv project my-gcp-project recurse ~/local-scripts/ gcp-instance-name:~/server-scripts/ zone us-east1-b
Answer: C
Reference: https://cloud.google.com/sdk/gcloud/reference/compute/copy-files
Question: 44
You are deploying your application to a Compute Engine virtual machine instance with the Stackdriver Monitoring Agent installed. Your application is a unix
process on the instance. You want to be alerted if the unix process has not run for at least 5 minutes. You are not able to change the application to generate
metrics or logs.
Which alert condition should you configure?
A. Uptime check
B. Process health
C. Metric absence
D. Metric threshold
Answer: B
Reference: https://cloud.google.com/monitoring/alerts/concepts-indepth
Question: 45
You have two tables in an ANSI-SQL compliant database with identical columns that you need to quickly combine into a single table, removing duplicate
rows from the result set.
What should you do?
A. Use the JOIN operator in SQL to combine the tables.
B. Use nested WITH statements to combine the tables.
C. Use the UNION operator in SQL to combine the tables.
D. Use the UNION ALL operator in SQL to combine the tables.
Answer: C
Reference: https://www.techonthenet.com/sql/union_all.php
Question: 46
Question: 47
Your website is deployed on Compute Engine. Your marketing team wants to test conversion rates between 3 different website designs.
Which approach should you use?
A. Deploy the website on App Engine and use traffic splitting.
B. Deploy the website on App Engine as three separate services.
C. Deploy the website on Cloud Functions and use traffic splitting.
D. Deploy the website on Cloud Functions as three separate functions.
Answer: A
Reference: https://cloud.google.com/appengine/docs/standard/python/splitting-traffic
Question: 48
You are planning to migrate a MySQL database to the managed Cloud SQL database for Google Cloud. You have Compute Engine virtual machine instances
that will connect with this Cloud SQL instance. You do not want to whitelist IPs for the Compute Engine instances to be able to access Cloud SQL.
What should you do?
A. Enable private IP for the Cloud SQL instance.
B. Whitelist a project to access Cloud SQL, and add Compute Engine instances in the whitelisted project.
C. Create a role in Cloud SQL that allows access to the database from external instances, and assign the Compute Engine instances to that role.
D. Create a CloudSQL instance on one project. Create Compute engine instances in a different project. Create a VPN between these two
projects to allow internal access to CloudSQL.
Answer: C
Reference: https://cloud.google.com/sql/docs/mysql/connect-external-app
Question: 49
You want to upload files from an on-premises virtual machine to Google Cloud Storage as part of a data migration. These files will be consumed by Cloud
DataProc Hadoop cluster in a GCP environment.
Which command should you use?
A. gsutil cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
B. gcloud cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
C. hadoop fs cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
D. gcloud dataproc cp [LOCAL_OBJECT] gs://[DESTINATION_BUCKET_NAME]/
Answer: A
Explanation:
The gsutil cp command allows you to copy data between your local file. storage. boto files generated by running "gsutil config"
Question: 50
You migrated your applications to Google Cloud Platform and kept your existing monitoring platform. You now find that your notification system is too
slow for time critical problems.
What should you do?
A. Replace your entire monitoring platform with Stackdriver.
B. Install the Stackdriver agents on your Compute Engine instances.
C. Use Stackdriver to capture and alert on logs, then ship them to your existing platform.
D. Migrate some traffic back to your old platform and perform AB testing on the two platforms concurrently.
Answer: B
Reference: https://cloud.google.com/monitoring/
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