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Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery.
Which three approaches can you take? (Choose three.)

  1. Disable writes to certain tables.
  2. Restrict access to tables by role.
  3. Ensure that the data is encrypted at all times.
  4. Restrict BigQuery API access to approved users.
  5. Segregate data across multiple tables or databases.
  6. Use Google Stackdriver Audit Logging to determine policy violations.

Answer(s): B,D,F

You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. 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?

  1. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
  2. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.
  3. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.
  4. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
  5. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

Answer(s): D

Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations.
What should you do?

  1. Add a node to the MySQL cluster and build an OLAP cube there.
  2. Use an ETL tool to load the data from MySQL into Google BigQuery.
  3. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
  4. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.

Answer(s): C

Your company has hired a new data scientist who wants to perform complicated analyses
across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks.
What should you do?

  1. Run a local version of Jupiter on the laptop.
  2. Grant the user access to Google Cloud Shell.
  3. Host a visualization tool on a VM on Google Compute Engine.
  4. Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

Answer(s): B

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madhan 6/16/2023 6:22:08 AM
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