Your company stores critical financial data in Cloud Storage. To ensure both high availability and disaster recovery, you need to choose a suitable storage location strategy. The company requires immediate access to data with minimal latency and must comply with data redundancy best practices, ensuring that data is available even in the event of a regional outage. Which of the following options best meets these requirements?

You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do? 

A company wants to migrate a large dataset of historical data from its on-premises storage to Cloud Storage. The dataset is around 100 TB, and the company has a limited network bandwidth, which would make online transfer highly time-consuming and resource-intensive. They need a solution that allows them to quickly and securely transfer this data to Google Cloud without causing disruptions to their ongoing operations. Which data transfer tool should the company choose?

A company wants to migrate its on-premises data to Google Cloud. They need a solution that will allow them to store structured and semi-structured data at scale, perform SQL-based analytics, and seamlessly integrate with Google Cloud’s AI and ML services. Which Google Cloud service is best suited for this requirement?

A retail company is using a variety of SaaS applications, including Google Ads and Salesforce, to collect and manage customer data. They want to extract data from these applications and load it directly into BigQuery on a daily basis for analytics purposes. The data must be automatically updated without manual intervention. Which Google Cloud service would best suit this requirement?