You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

• A. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
• B. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
• C. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
• D. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
• E. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
• F. Merge the transformed tables together with a SQL query

Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

• A. Change "person" to "person.city".
• B. Add ", UNNEST(city)" before the WHERE clause.
• C. Change "person" to "city.person".
• D. Add ", UNNEST(person)" before the WHERE clause.

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 WHERE_PARTITIONTIME pseudo column
• B. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
• C. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
• D. Use the TABLE_DATE_RANGE function

You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

• A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
• B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
• C. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
• D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

• A. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
• B. Create a service account and grant dataset access to that accoun
• C. Create a dummy user and grant dataset access to that use
• D. Use the service account's private key to access the dataset
• E. Create groups for your users and give those groups access to the dataset
• F. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request