MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

• A. Rowkey: dateColumn data: device_id, data_point
• B. Rowkey: data_pointColumn data: device_id, date
• C. Rowkey: date#device_idColumn data: data_point
• D. Rowkey: device_idColumn data: date, data_point
• E. Rowkey: date#data_pointColumn data: device_id

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

• A. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
• B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
• C. Consume the stream of data in Cloud Dataflow using Kafka I
• D. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
• E. If that number falls below 4000, send an alert.
• F. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
• G. If that number falls below 4000, send an alert.
• H. Set a sliding time window of 1 hour every 5 minute
• I. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
• J. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
• K. Consume the stream of data in Cloud Dataflow using Kafka I
• L. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
• M. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

• A. Hive
• B. YARN
• C. Spark
• D. Pig

You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

• A. Denormalize the data as must as possible.
• B. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
• C. Preserve the structure of the data as much as possible.
• D. Use BigQuery'ssupport for external data sources to query.
• E. Use BigQuery UPDATE to further reduce the size of the dataset.
• F. Develop a data pipeline where status updates are appended to BigQuery instead of updated.

You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

• A. Cloud Scheduler
• B. Workflow Templates on Cloud Dataproc
• C. cron
• D. Cloud Composer