Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular.You need to design a storage system that is simple and cost-effective. What should you do?
Answer(s): C
Creating a single-region bucket with custom Object Lifecycle Management policies based on upload date is the most appropriate solution. This approach allows you to automatically transition objects to less expensive storage classes as their access frequency decreases over time. For example, frequently accessed files can remain in the Standard storage class initially, then transition to Nearline, Coldline, or Archive storage as their popularity wanes. This strategy ensures a cost-effective and efficient storage system while maintaining simplicity by automating the lifecycle management of video files.
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?
Answer(s): A
The Dataflow Developer role provides the necessary permissions to manage Dataflow streaming pipelines, including the ability to restart pipelines. This role adheres to the principle of least privilege, as it grants only the permissions required to manage and operate Dataflow jobs without unnecessary administrative access. Other roles, such as Dataflow Admin, would grant broader permissions, which are not needed in this scenario.
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:· Data is streamed from Pub/Sub and is processed in real-time.· Data is transformed before being stored.· Data is stored in a location that will allow it to be analyzed with SQL using Looker.Which Google Cloud services should you recommend for the pipeline?
Answer(s): D
To build a serverless data pipeline that processes data in real-time from Pub/Sub, transforms it, and stores it for SQL-based analysis using Looker, the best solution is to use Dataflow and BigQuery. Dataflow is a fully managed service for real-time data processing and transformation, while BigQuery is a serverless data warehouse that supports SQL-based querying and integrates seamlessly with Looker for data analysis and visualization. This combination meets the requirements for real-time streaming, transformation, and efficient storage for analytical queries.
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?
Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.
You have a BigQuery dataset containing sales dat
Answer(s): B
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data to Coldline storage minimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.
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