You are designing the storage architecture for a financial analytics platform. The platform ingests and stores terabytes of transactional data daily, which is used for both real-time fraud detection and long-term historical analysis. Transaction data from the last 30 days must be accessible with very low latency for the fraud detection engine. Data older than 30 days is accessed infrequently for quarterly reports, where retrieval times of a few seconds are acceptable. All data must be retained for five years to meet compliance regulations. You need to design a solution as cost-effective as possible. What should you do?

• A. Ingest all data into BigQuery using time-partitioned tables, and rely on BigQuery ' s automatic long- term storage pricing for data older than 90 days.
• B. Configure a Cloud Storage bucket with an Object Lifecycle Management policy to transition data from the Standard class to the Archive class after 30 days.
• C. Store all transaction data in a Cloud Storage bucket using the Standard storage class for the entire five- year retention period.
• D. Configure a Cloud Storage bucket with an Object Lifecycle Management policy to transition data from the Standard class to the Coldline class after 30 days.

Your operations team currently stores 10 TB of data m an object storage service from a third-party provider.
They want to move this data to a Cloud Storage bucket as quickly as possible, following Google- recommended practices. They want to minimize the cost of this data migration. When approach should they use?

• A. Download the data to the on-premises data center and upload it to the Cloud Storage bucket
• B. Download the data to a Transfer Appliance and ship it to Google
• C. Use the Storage Transfer Service to move the data
• D. Use the gsutil mv command lo move the data

Your development team has created a mobile game app. You want to test the new mobile app on Android and iOS devices with a variety of configurations. You need to ensure that testing is efficient and cost-effective.
What
should you do?

• A. Upload your mobile app with different configurations to Firebase Hosting and test each configuration.
• B. Create Android and iOS VMs on Google Cloud, install the mobile app on the VMs, and test the mobile app.
• C. Create Android and iOS containers on Google Kubernetes Engine (GKE), install the mobile app on the containers, and test the mobile app.
• D. Upload your mobile app to the Firebase Test Lab, and test the mobile app on Android and iOS devices.

For this question, refer to the Dress4Win case study.
As part of their new application experience, Dress4Wm allows customers to upload images of themselves.
The customer has exclusive control over who may view these images. Customers should be able to upload images with minimal latency and also be shown their images quickly on the main application page when they log in. Which configuration should Dress4Win use?

• A. Store image files in a Google Cloud Storage bucket. Use Google Cloud Datastore to maintain metadata that maps each customer ' s ID and their image files.
• B. Use a distributed file system to store customers ' images. As storage needs increase, add more persistent disks and/or nodes. Use a Google Cloud SQL database to maintain metadata that maps each customer ' s ID to their image files.
• C. Use a distributed file system to store customers ' images. As storage needs increase, add more persistent disks and/or nodes. Assign each customer a unique ID, which sets each file ' s owner attribute, ensuring privacy of images.
• D. Store image files in a Google Cloud Storage bucket. Add custom metadata to the uploaded images in Cloud Storage that contains the customer ' s unique ID.

You are developing an application using different microservices that should remain internal to the cluster.
You want to be able to configure each microservice with a specific number of replicas. You also want to be able to address a specific microservice from any other microservice in a uniform way, regardless of the number of replicas the microservice scales to. You need to implement this solution on Google Kubernetes Engine. What should you do?

• A. Deploy each microservice as a Pod. Expose the Pod in the cluster using an Ingress, and use the Ingress IP address name to address the Pod from other microservices within the cluster.
• B. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using an Ingress, and use the Ingress IP address to address the Deployment from other microservices within the cluster.
• C. Deploy each microservice as a Deployment. Expose the Deployment in the cluster using a Service, and use the Service DNS name to address it from other microservices within the cluster.
• D. Deploy each microservice as a Pod. Expose the Pod in the cluster using a Service, and use the Service DNS name to address the microservice from other microservices within the cluster.