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NEW QUESTION 21
The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.
- A. BigQuery Data Transfer Service
- B. BiqQuery API
- C. Cloud Dataflow connector
- D. DataFlow SDK
The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations.
NEW QUESTION 22
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO.
During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?
- A. Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
- B. Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
- C. Check the dashboard application to see if it is not displaying correctly.
- D. Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.
Stackdriver can be used to check the error like number of unack messages, publisher pushing messages faster.
NEW QUESTION 23
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
- B. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- D. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
NEW QUESTION 24
You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time.
What should you do?
- A. Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.
- B. Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
- C. Send the data to Google Cloud Datastore and then export to BigQuery.
- D. Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.
Pubsub for realtime, Dataflow for pipeline, Bigquery for analytics.
NEW QUESTION 25
When you design a Google Cloud Bigtable schema it is recommended that you _________.
- A. Avoid schema designs that are based on NoSQL concepts
- B. Avoid schema designs that require atomicity across rows
- C. Create schema designs that require atomicity across rows
- D. Create schema designs that are based on a relational database design
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
NEW QUESTION 26
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