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Google Professional-Cloud-Developer exam consists of 50 multiple-choice and multiple-select questions that must be completed within two hours. Professional-Cloud-Developer exam covers various topics related to the development of cloud-based applications, including application deployment, security, and scalability. Professional-Cloud-Developer Exam is designed to test the candidate's ability to develop and implement cloud-based solutions on the Google Cloud Platform.

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Google Professional-Cloud-Developer Certification is recognized globally and is highly valued in the cloud computing industry. Google Certified Professional - Cloud Developer certification provides a competitive edge to software developers and helps them to stand out in the job market. In addition, this certification also helps organizations to identify and hire skilled professionals who can develop and deploy applications on GCP. By becoming a Google Certified Professional - Cloud Developer, individuals can demonstrate their commitment to staying current with the latest cloud technologies and their ability to design, develop, and manage cloud applications on GCP.

Google Certified Professional - Cloud Developer Sample Questions (Q105-Q110):

NEW QUESTION # 105
You need to configure a Deployment on Google Kubernetes Engine (GKE). You want to include a check that verifies that the containers can connect to the database. If the Pod is failing to connect, you want a script on the container to run to complete a graceful shutdown. How should you configure the Deployment?

A. Create the Deployment with an initContainer that checks the service availability. Configure a Prestop lifecycle handler that runs the shutdown script if the Pod is failing.
B. Create two jobs: one that checks whether the container can connect to the database, and another that runs the shutdown script if the Pod is failing.
C. Create the Deployment with a PostStart lifecycle handler that checks the service availability. Configure a PreStop lifecycle handler that runs the shutdown script if the container is failing.
D. Create the Deployment with a livenessProbe for the container that will fail if the container can't connect to the database. Configure a Prestop lifecycle handler that runs the shutdown script if the container is failing.

Answer: D

Explanation:
Explanation
https://cloud.google.com/architecture/best-practices-for-running-cost-effective-kubernetes-applications-on-gke#m

NEW QUESTION # 106
Your company's product team has a new requirement based on customer demand to autoscale your stateless and distributed service running in a Google Kubernetes Engine (GKE) duster. You want to find a solution that minimizes changes because this feature will go live in two weeks. What should you do?

A. Deploy a Horizontal Pod Autoscaler, and scale based on a custom metric.
B. Deploy a Vertical Pod Autoscaler, and scale based on a custom metric.
C. Deploy a Vertical Pod Autoscaler, and scale based on the CPU load.
D. Deploy a Horizontal Pod Autoscaler, and scale based on the CPU toad.

Answer: D

NEW QUESTION # 107
You recently developed a new service on Cloud Run. The new service authenticates using a custom service and then writes transactional information to a Cloud Spanner database. You need to verify that your application can support up to 5,000 read and 1,000 write transactions per second while identifying any bottlenecks that occur. Your test infrastructure must be able to autoscale. What should you do?

A. Create a Cloud Task to generate a test load. Use Cloud Scheduler to run 60,000 Cloud Task transactions per minute for 10 minutes. Analyze the results using Cloud Monitoring.
B. Create a Compute Engine instance that uses a LAMP stack image from the Marketplace, and use Apache Bench to generate load tests against the service. Analyze the results using Cloud Trace.
C. Build a test harness to generate requests and deploy it to Cloud Run. Analyze the VPC Flow Logs using Cloud Logging.
D. Create a Google Kubernetes Engine cluster running the Locust or JMeter images to dynamically generate load tests. Analyze the results using Cloud Trace.

Answer: D

Explanation:
Explanation
https://cloud.google.com/architecture/distributed-load-testing-using-gke

NEW QUESTION # 108
You are a developer at a company that operates an ecommerce website. The website stores the customer order data in a Cloud SQL for PostgreSQL database. Data scientists on the marketing team access this data to run their reports. Every time they run these reports, the website's performance is negatively affected. You want to provide access to up-to-date customer order datasets without affecting your website. What should you do?

A. Set up a Bigtable table for the data science team. Configure the application to perform dual writes to both Cloud SQL and Bigtable simultaneously.
B. Configure Cloud Scheduler to run an hourly Cloud Run function that exports the data from the Cloud SQL database into CSV format and sends the data to a Cloud Storage bucket.
C. Set up a BigQuery dataset for the data science team. Configure Datastream to replicate the relevant Cloud SQL tables in BigQuery.
D. Create a clone of the PostgreSQL database instance for the data science team. Schedule a job to create a new clone every 15 minutes.

Answer: C

Explanation:
https://careerfoundry.com/en/blog/data-analytics/where-to-find-free-datasets/

NEW QUESTION # 109
You are developing an online chat application where users can upload profile pictures. Uploaded profile pictures must comply with content policies. You need to detect inappropriate images and label those images automatically when they are uploaded. In the future, this process will need to be expanded to include additional processing tasks such as watermarking and image compression.
You want to simplify orchestration and minimize operational overhead of the image scanning and labeling steps while also ensuring that additional steps can be added and removed easily later on. What should you do?

A. Save user-uploaded images to a Cloud Storage bucket. Configure a Cloud Run function that is triggered when a new image is uploaded and calls one or more Cloud Run services. Create additional Cloud Run services that call the Vision API to process each new uploaded image.
B. Save user-uploaded images to a Cloud Storage bucket. Create an Eventarc trigger that connects the bucket to the Workflows event receiver when a new image is uploaded. Create a workflow in Workflows with multiple Cloud Run functions that call the Vision API to process each new uploaded image.
C. Save user-uploaded images to a Cloud Storage bucket. Configure a Cloud Run function that is triggered when a new image is uploaded and publishes a message to a Pub/Sub topic. Deploy microservices in GKE that subscribe to the Pub/Sub topic and call the Vision API to process each new uploaded image.
D. Save user-uploaded images to a temporary Cloud Storage bucket. Implement code on the backend server to retrieve the image content and call the Vision API to process each new uploaded image.

Answer: B

Explanation:
Using Eventarc to trigger Workflows when a new image is uploaded provides a scalable and low- overhead solution for orchestrating multiple steps in the image processing pipeline. Workflows allows you to define a sequence of tasks (e.g., content moderation, watermarking, compression) that can be easily modified or extended as requirements evolve. Each task can be managed by Cloud Functions within the workflow, making it straightforward to add or remove steps in the future.
This approach minimizes operational overhead by using a serverless workflow for orchestration rather than managing infrastructure directly. The other options either involve more complex orchestration setups or lack the flexibility to easily expand to additional processing tasks as needed.

NEW QUESTION # 110
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