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NEW QUESTION 34
You’re using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You’ve recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload.
What should you do?

  • A. Add a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
  • B. Add a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
  • C. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
  • D. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

Answer: B

 

NEW QUESTION 35
You have spent a few days loading data from comma-separated values (CSV) files into the Google
BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience,
you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute
web session durations of users who visit your site, and you want to change its data type to the
TIMESTAMP. You want to minimize the migration effort without making future queries computationally
expensive. What should you do?

  • A. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to
    cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table
    NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table
    NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is
    loaded into the table NEW_CLICK_STREAM.
  • B. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
    Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.
  • C. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric
    values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now
    on.
  • D. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the
    BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto
    true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause
    ensuring that the value of IS_NEWmust be true.
  • E. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP
    type. Reload the data.

Answer: D

 

NEW QUESTION 36
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat
a. Which Stackdriver alerts should you create?

  • A. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
  • B. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination
  • C. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
  • D. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination

Answer: C

 

NEW QUESTION 37
You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

  • A. Write a Cloud Dataflow pipeline that aggregates all data in session windows.
  • B. Have the data acquisition devices publish data to Cloud Pub/Sub.
  • C. Deploy small Kafka clusters in your data centers to buffer events.
  • D. Establish a Cloud Interconnect between all remote data centers and Google.

Answer: B

 

NEW QUESTION 38
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD.You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

  • A. Use WHEREdate BETWEEN YYYY-MM-DD AND YYYY-MM-DD
  • B. Use the WHERE_PARTITIONTIMEpseudo column
  • C. Use the TABLE_DATE_RANGEfunction
  • D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

Answer: C

Explanation:
Explanation/Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understand- your-mobile-app?hl=am

 

NEW QUESTION 39
……

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