- (Exam Topic 5)
You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

1. A. Cancel
2. B. Drain
3. C. Stop
4. D. Finish

Correct Answer: B
Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.
Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline

- (Exam Topic 6)
You are updating the code for a subscriber to a Put/Sub feed. You are concerned that upon deployment the subscriber may erroneously acknowledge messages, leading to message loss. You subscriber is not set up to retain acknowledged messages. What should you do to ensure that you can recover from errors after deployment?

1. A. Use Cloud Build for your deployment if an error occurs after deployment, use a Seek operation to locate a tmestamp logged by Cloud Build at the start of the deployment
2. B. Create a Pub/Sub snapshot before deploying new subscriber cod
3. C. Use a Seek operation to re-deliver messages that became available after the snapshot was created
4. D. Set up the Pub/Sub emulator on your local machine Validate the behavior of your new subscriber togs before deploying it to production
5. E. Enable dead-lettering on the Pub/Sub topic to capture messages that aren't successful acknowledged if an error occurs after deployment, re-deliver any messages captured by the dead-letter queue

Correct Answer: B

- (Exam Topic 6)
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

1. A. Create a table in BigQuery, and append the new samples for CPU and memory to the table
2. B. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
3. C. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
4. D. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

Correct Answer: C
A tall and narrow table has a small number of events per row, which could be just one event, whereas a short and wide table has a large number of events per row. As explained in a moment, tall and narrow tables are best suited for time-series data. For time series, you should generally use tall and narrow tables. This is for two reasons: Storing one event per row makes it easier to run queries against your data. Storing many events per row makes it more likely that the total row size will exceed the recommended maximum (see Rows can be big but are not infinite).
https://cloud.google.com/bigtable/docs/schema-design-time-series#patterns_for_row_key_design

- (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

- (Exam Topic 6)
You currently have a single on-premises Kafka cluster in a data center in the us-east region that is responsible for ingesting messages from IoT devices globally. Because large parts of globe have poor internet connectivity, messages sometimes batch at the edge, come in all at once, and cause a spike in load on your Kafka cluster. This is becoming difficult to manage and prohibitively expensive. What is the
Google-recommended cloud native architecture for this scenario?

1. A. Edge TPUs as sensor devices for storing and transmitting the messages.
2. B. Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.
3. C. An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.
4. D. A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

Correct Answer: C