Google : Professional Cloud Developer Certification 2021

Google : Professional Cloud Developer Certification 2021

Google : Professional Cloud Developer Certification 2021

best practice Tests for Professional Cloud Developer Certification 2021

Language: english

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Instructor(s): Salem Salem

Google : Professional Cloud Developer Certification 2021



Professional Cloud Developers build scalable and highly available applications using Google-recommended practices and tools. They have experience with cloud-native applications, developer tools, managed services, and next-generation databases. Cloud Developers are also proficient with at least one general-purpose programming language and are skilled at producing meaningful metrics and logs to debug and trace code.

The Professional Cloud Developer certification exam is a 2-hour test covering the multiple-choice and multiple-select questions. It can be taken in English or Japanese and is available as an online proctored and an on-site proctored exam. The registration process costs $200, and this is for the delivery of a single test. The candidates who do not pass the exam must retake it to obtain the associated certificate. To do this, they must pay a new exam fee for their retake.

This certification exam measures your ability to perform various tasks required for the successful functioning of different processes. The details of its topics are enumerated below:

Designing Highly Available, Reliable, and Scalable Cloud-Native Apps

Building & Testing Apps

Deploying Apps

Incorporating Google Cloud Services

Managing Application Performance Monitoring

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

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


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

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 asynchronous applications (e.g., Apache Kafka, Pub/Sub)

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 and keys (e.g., Cloud KMS, HashiCorp Vault)

Authenticating to Google services (e.g., application default credentials, JSON Web Token (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)

Running services with least privileged access (e.g., Workload Identity)

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

Google-recommended practices and documentation

1.3 Managing application data. Considerations include:

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

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

Time-limited access to objects

Data retention requirements

Structured vs. unstructured data

Strong vs. eventual consistency

Data volume

Frequency of data access in Cloud Storage

Google-recommended practices and documentation

1.4 Application modernization. Considerations include:

Using managed services

Refactoring a monolith to microservices

Designing stateless, horizontally scalable services

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

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

Using developer tooling (e.g., Cloud Code, Skaffold)

2.2 Writing efficient code. Considerations include:

Algorithm design

Modern application patterns

Software development methodologies

Debugging and profiling code

2.3 Testing. Considerations include:

Unit testing

Integration testing

Performance testing

Load testing

2.4 Building. Considerations include:

Source control management

Creating secure 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 efficiency

Section 3: Deploying applications

3.1 Recommend appropriate deployment strategies using the appropriate tools (e.g., Cloud Build, Spinnaker, Tekton, Anthos Configuration Manager) for the target compute environment (e.g., Compute Engine, Google Kubernetes Engine). Considerations include:

Blue/green deployments

Traffic-splitting deployments

Rolling deployments

Canary deployments

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

Installing an application into a virtual machine (VM)

Managing service accounts for VMs

Bootstrapping applications

Exporting application logs and metrics

Managing Compute Engine VM images and binaries

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

Deploying a containerized application to GKE

Managing Kubernetes RBAC and Google Cloud IAM relationships

Configuring Kubernetes namespaces

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 life cycle

Define Kubernetes resources and configurations

3.4 Deploying a Cloud Function. Considerations include:

Cloud Functions that are triggered via an event from Google Cloud services (e.g., Pub/Sub, Cloud Storage objects)

Cloud Functions that are invoked via HTTP

Securing Cloud Functions

3.5 Using service accounts. Considerations 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 services

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

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

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

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

Storing and retrieving objects from Cloud Storage

4.2 Integrating an application with compute services. Considerations include:

Implementing service discovery in GKE and Compute Engine

Reading instance metadata to obtain application configuration

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

Authenticating to Cloud APIs with Workload Identity

4.3 Integrating Cloud APIs with applications. Considerations include:

Enabling a Cloud API

Making API calls using supported options (e.g., Cloud Client Library, REST API or gRPC, 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 Cloud API calls

Section 5: Managing application performance monitoring

5.1 Managing Compute Engine VMs. Considerations include:

Debugging a custom VM image using the serial port

Diagnosing a failed Compute Engine VM startup

Sending logs from a VM to Cloud Logging

Viewing and analyzing logs

Inspecting resource utilization over time

5.2 Managing Google Kubernetes Engine workloads. Considerations include:

Configuring logging and monitoring

Analyzing container life cycle events (e.g., CrashLoopBackOff, ImagePullErr)

Viewing and analyzing logs

Writing and exporting custom metrics

Using external metrics and corresponding alerts

Configuring workload autoscaling

5.3 Troubleshooting application performance. Considerations include:

Creating a monitoring dashboard

Writing custom metrics and creating log-based metrics

Using Cloud Debugger

Reviewing stack traces for error analysis

Exporting logs from Google Cloud

Viewing logs in the Google Cloud Console

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

Monitoring and profiling a running application

Using documentation, forums, and Google Cloud support


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