Data 278-287

What should you do?

• Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly

• Establish a BigQuery quota for the marketing team, and limit the maximum number of bytes scanned each day.

• Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly

• Create a BigQuery Standard pay-as-you go reservation with a baseline of 0 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.

What should you do?

• Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.

• Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

• Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.

• Move your data to Cloud Storage. Run your jobs on Dataproc.

You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?

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How should you store this data?

• Store your data in BigQuery. Concatenate the sensor ID and timestamp, and use it as the primary key.

• Store your data in Bigtable. Concatenate the sensor ID and timestamp and use it as the row key. Perform an export to BigQuery every day.

• Store your data in Bigtable. Concatenate the sensor ID and metric, and use it as the row key. Perform an export to BigQuery every day.

• . Store your data in BigQuery. Use the metric as a primary key.

You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?

• Change the storage class of the Hive partitioned data objects from Coldline to Standard.

• Create an individual external table for each Hive partition by using a common table name prefix. Use wildcard table queries to reference the partitioned data.

• Upgrade the external table to a BigLake table. Enable metadata caching for the table.

• Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

What should you do?

• Use the BigQuery Storage Write API and ensure that your target BigQuery table is regional.

• Use the BigQuery Storage Write API and ensure that your target BigQuery table is multiregional.

• Use the BigQuery Streaming API and ensure that your target BigQuery table is regional.

• Use the BigQuery Streaming API and ensure that your target BigQuery table is multiregional.

What should you do?

• Set the expiring values of the column families to 29 days and keep the number of versions to 1.

• Use a timestamp range filter in the query to fetch the customer's data for a specific range.

• Schedule a job daily to scan the data in the table and delete data older than 30 days.

• Set the expiring values of the column families to 30 days and set the number of versions to 2.

What should you do?

• Use watermarks to define the expected data arrival window. Allow late data as it arrives.

• Change your windowing function to tumbling windows to avoid overlapping window periods.

• Change your windowing function to session windows to define your windows based on certain activity.

• Expand your hopping window so that the late data has more time to arrive within the grouping

What should you do?

• Give analysts the BigQuery Data Viewer role at the project level. Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

• Give analysts the BigQuery Data Viewer role at the project level. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.

• Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset

• Give analysts the BigQuery Data Viewer role on the shared dataset. Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.

What should you do?

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