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NEW QUESTION 30
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

A. Retry the query every second until it comes back online to minimize staleness of data.B. Issue a command to restart the database servers.C. Retry the query with exponential backoff, up to a cap of 15 minutes.D. Reduce the query frequency to once every hour until the database comes back online.

Answer: C

Explanation:
https://cloud.google.com/sql/docs/mysql/manage-connections

 

NEW QUESTION 31
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity `Movie' the property `actors' and the property
`tags' have multiple values but the property `date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Option CB. Option AC. Option DD. Option B.

Answer: B

 

NEW QUESTION 32
You need ads data to serve Al models and historical data tor analytics longtail and outlier data points need to be identified You want to cleanse the data n near-reel time before running it through Al models What should you do?

A. Use Cloud Storage as a data warehouse shell scripts tor processing, and BigQuery to create views tor desired datasetsB. Use Dataflow to identity longtail and outber data points programmatically with BigQuery as a sinkC. Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQueryD. Use BigQuery to ingest prepare and then analyze the data and then run queries to create views

Answer: D

 

NEW QUESTION 33
An organization maintains a Google BigQuery dataset that contains tables with user-level data. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user- level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

A. Create and share a new dataset and view that provides the aggregate results.B. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.C. Create and share an authorized view that provides the aggregate results.D. Create and share a new dataset and table that contains the aggregate results.

Answer: B

Explanation:
Authorized view is used to protect the underlying data, authorized views are created in different dataset with restricted access.

 

NEW QUESTION 34
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.C. Add capacity (memory and disk space) to the database server by the order of 200.D. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Answer: B

 

NEW QUESTION 35
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