- (Exam Topic 4)
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

1. A. The CSV data loaded in BigQuery is not flagged as CSV.
2. B. The CSV data has invalid rows that were skipped on import.
3. C. The CSV data loaded in BigQuery is not using BigQuery’s default encoding.
4. D. The CSV data has not gone through an ETL phase before loading into BigQuery.

Correct Answer: B

- (Exam Topic 5)
Which of the following statements about Legacy SQL and Standard SQL is not true?

1. A. Standard SQL is the preferred query language for BigQuery.
2. B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
3. C. One difference between the two query languages is how you specify fully-qualified table names (i.
4. D. table names that include their associated project name).
5. E. You need to set a query language for each dataset and the default is Standard SQL.

Correct Answer: D
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released.
In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql

- (Exam Topic 1)
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. A. Disable writes to certain tables.
2. B. Restrict access to tables by role.
3. C. Ensure that the data is encrypted at all times.
4. D. Restrict BigQuery API access to approved users.
5. E. Segregate data across multiple tables or databases.
6. F. Use Google Stackdriver Audit Logging to determine policy violations.

Correct Answer: BDF

- (Exam Topic 6)
Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

1. A. Set a single global window to capture all the data.
2. B. Set sliding windows to capture all the lagged data.
3. C. Use watermarks and timestamps to capture the lagged data.
4. D. Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

Correct Answer: B

- (Exam Topic 4)
You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

1. A. Change the processing job to use Google Cloud Dataproc instead.
2. B. Manually start the Cloud Dataflow job each morning when you get into the office.
3. C. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
4. D. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Correct Answer: C