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NEW QUESTION 22
Business owners at your company have given you a database of bank transactions. Each row contains the
user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what
type of machine learning can be applied to the data. Which three machine learning applications can you
use? (Choose three.)

  • A. Unsupervised learning to predict the location of a transaction.
  • B. Reinforcement learning to predict the location of a transaction.
  • C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
  • D. Supervised learning to determine which transactions are most likely to be fraudulent.
  • E. Supervised learning to predict the location of a transaction.
  • F. Clustering to divide the transactions into N categories based on feature similarity.

Answer: B,C,F

 

NEW QUESTION 23
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 the TABLE_DATE_RANGEfunction
  • B. Use the WHERE_PARTITIONTIMEpseudo column
  • C. Use WHEREdate BETWEEN YYYY-MM-DD AND YYYY-MM-DD
  • D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

Answer: A

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

 

NEW QUESTION 24
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

  • A. Create separate views to cover each month, and query from these views
  • B. Enable query caching so you can cache data from previous months
  • C. Convert the sharded tables into a single partitioned table
  • D. Convert all daily log tables into date-partitioned tables

Answer: C

Explanation:
https://cloud.google.com/bigquery/docs/creating-partitioned-tables#converting_date- sharded_tables_into_ingestion-time_partitioned_tables

 

NEW QUESTION 25
Cloud Dataproc charges you only for what you really use with _____ billing.

  • A. hour-by-hour
  • B. month-by-month
  • C. minute-by-minute
  • D. week-by-week

Answer: C

Explanation:
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.

 

NEW QUESTION 26
The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

  • A. Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.
  • B. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
  • C. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
  • D. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.

Answer: A

 

NEW QUESTION 27
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