Professional Cloud Developer Exam Dumps

Google-PCD Exam Format | Course Contents | Course Outline | Exam Syllabus | Exam 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 exam 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

- Reading 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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Google-PCD PDF Sample Questions

Google-PCD Sample Questions

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Google-PCD
Professional Cloud Developer
https://killexams.com/pass4sure/exam-detail/Google-PCD
Question: 39
Your company is planning to migrate their on-premises Hadoop environment to the cloud. Increasing storage cost and
maintenance of data stored in HDFS is a major concern for your company. You also want to make minimal changes to
existing data analytics jobs and existing architecture.
How should you proceed with the migration?
A. Migrate your data stored in Hadoop to BigQuery. Change your jobs to source their information from BigQuery
instead of the on-premises Hadoop environment.
B. Create Compute Engine instances with HDD instead of SSD to save costs. Then perform a full migration of your
existing environment into the new one in Compute Engine instances.
C. Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop environment to the new
Cloud Dataproc cluster. Move your HDFS data into larger HDD disks to save on storage costs.
D. Create a Cloud Dataproc cluster on Google Cloud Platform, and then migrate your Hadoop code objects to the new
cluster. Move your data to Cloud Storage and leverage the Cloud Dataproc connector to run jobs on that data.
Answer: D
Question: 40
HipLocal�s data science team wants to analyze user reviews.
How should they prepare the data?
A. Use the Cloud Data Loss Prevention API for redaction of the review dataset.
B. Use the Cloud Data Loss Prevention API for de-identification of the review dataset.
C. Use the Cloud Natural Language Processing API for redaction of the review dataset.
D. Use the Cloud Natural Language Processing API for de-identification of the review
dataset.
Answer: B
Explanation:
https://cloud.google.com/dlp/docs/deidentify-sensitive-data
Question: 41
You are developing a microservice-based application that will be deployed on a Google Kubernetes Engine cluster.
The application needs to read and write to a Spanner database. You want to follow security best practices while
minimizing code changes.
How should you configure your application to retrieve Spanner credentials?
A. Configure the appropriate service accounts, and use Workload Identity to run the pods.
B. Store the application credentials as Kubernetes Secrets, and expose them as environment variables.
C. Configure the appropriate routing rules, and use a VPC-native cluster to directly connect to the database.
D. Store the application credentials using Cloud Key Management Service, and retrieve them whenever a database
connection is made.
Answer: A
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/workload-identity
Question: 42
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
Explanation:
Reference: https://www.techonthenet.com/sql/union_all.php
Question: 43
Your company has deployed a new API to App Engine Standard environment. During testing, the API is not behaving
as expected. You want to monitor the application over time to diagnose the problem within the application code
without redeploying the application.
Which tool should you use?
A. Stackdriver Trace
B. Stackdriver Monitoring
C. Stackdriver Debug Snapshots
D. Stackdriver Debug Logpoints
Answer: D
Question: 44
Which database should HipLocal use for storing user activity?
A. BigQuery
B. Cloud SQL
C. Cloud Spanner
D. Cloud Datastore
Answer: A
Question: 45
You have containerized a legacy application that stores its configuration on an NFS share. You need to deploy this
application to Google Kubernetes Engine (GKE) and do not want the application serving traffic until after the
configuration has been retrieved.
What should you do?
A. Use the gsutil utility to copy files from within the Docker container at startup, and start the service using an
ENTRYPOINT script.
B. Create a PersistentVolumeClaim on the GKE cluster. Access the configuration files from the volume, and start the
service using an ENTRYPOINT script.
C. Use the COPY statement in the Dockerfile to load the configuration into the container image. Verify that the
configuration is available, and start the service using an ENTRYPOINT script.
D. Add a startup script to the GKE instance group to mount the NFS share at node startup. Copy the configuration files
into the container, and start the service using an ENTRYPOINT script.
Answer: D
Explanation:
Reference: https://cloud.google.com/compute/docs/instances/startup-scripts/linux
Question: 46
You are developing a microservice-based application that will be deployed on a Google Kubernetes Engine cluster.
The application needs to read and write to a Spanner database. You want to follow security best practices while
minimizing code changes.
How should you configure your application to retrieve Spanner credentials?
A. Configure the appropriate service accounts, and use Workload Identity to run the pods.
B. Store the application credentials as Kubernetes Secrets, and expose them as environment variables.
C. Configure the appropriate routing rules, and use a VPC-native cluster to directly connect to the database.
D. Store the application credentials using Cloud Key Management Service, and retrieve them whenever a database
connection is made.
Answer: A
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/workload-identity
Question: 47
Your application is built as a custom machine image. You have multiple unique deployments of the machine image.
Each deployment is a separate managed instance group with its own template. Each deployment requires a unique set
of configuration values. You want to provide these unique values to each deployment but use the same custom
machine image in all deployments. You want to use out-of-the-box features of Compute Engine.
What should you do?
A. Place the unique configuration values in the persistent disk.
B. Place the unique configuration values in a Cloud Bigtable table.
C. Place the unique configuration values in the instance template startup script.
D. Place the unique configuration values in the instance template instance metadata.
Answer: D
Explanation:
Reference: https://cloud.google.com/compute/docs/instance-groups
Question: 48
You are developing a microservice-based application that will be deployed on a Google Kubernetes Engine cluster.
The application needs to read and write to a Spanner database. You want to follow security best practices while
minimizing code changes.
How should you configure your application to retrieve Spanner credentials?
A. Configure the appropriate service accounts, and use Workload Identity to run the pods.
B. Store the application credentials as Kubernetes Secrets, and expose them as environment variables.
C. Configure the appropriate routing rules, and use a VPC-native cluster to directly connect to the database.
D. Store the application credentials using Cloud Key Management Service, and retrieve them whenever a database
connection is made.
Answer: A
Explanation:
https://cloud.google.com/kubernetes-engine/docs/concepts/workload-identity
Question: 49
You have an application running in App Engine. Your application is instrumented with Stackdriver Trace. The
/product-details request reports details about four known unique products at /sku-details as shown below. You want to
reduce the time it takes for the request to complete.
What should you do?
A. Increase the size of the instance class.
B. Change the Persistent Disk type to SS
D. Change /product-details to perform the requests in parallel.
E. Store the /sku-details information in a database, and replace the webservice call with a database query.
Answer: C
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Google updates privacy coverage to educate its AI on everything you publish online

Calvin Wankhede / Android Authority

TL;DR

You probably didn’t notice, but Google quietly updated its privacy coverage over the weekend. whereas the wording of the policy is just a bit different from before, the alternate is adequate to be concerning.

As found out by way of Gizmodo, Google has up-to-date its privacy coverage. while there’s nothing principally extraordinary in most of the coverage, one part now stands proud — the analysis and building area. That area explains how Google can use your assistance and now reads as:

Google uses assistance to enrich our capabilities and to improve new items, elements and applied sciences that benefit our users and the public. as an example, we use publicly obtainable suggestions to assist instruct Google’s AI models and construct products and contours like Google Translate, Bard, and Cloud AI capabilities.

before the replace, this part outlined “for language models” as an alternative of “AI fashions.” It additionally most effective mentioned Google Translate, where it now provides Bard and Cloud AI.

as the outlet points out, this is a odd clause for a corporation to add. The reason it’s unusual is that the style it’s worded makes it sound as if the tech colossal reserves the right to reap and use information from any part of the public web. always, a policy such as this handiest discusses how the enterprise will use records posted on its own features.

whereas most americans seemingly understand that whatever they put on-line can be publicly available, this development opens up a new twist — use. It’s now not essentially others being able to see what you write online, but also about how that records may be used.

Bard, ChatGPT, Bing Chat, and other AI fashions that provide precise-time suggestions work via scraping assistance from the information superhighway. The sourced tips can regularly come from others’ intellectual property. at this time, there are court cases accusing these AI tools of theft, and there are more likely to be greater to come down the road.

References

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