- (Exam Topic 5)
What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

1. A. create a third instance and sync the data from the two storage types via batch jobs
2. B. export the data from the existing instance and import the data into a new instance
3. C. run parallel instances where one is HDD and the other is SDD
4. D. the selection is final and you must resume using the same storage type

Correct Answer: B
When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster.
If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write
a Cloud Dataflow or Hadoop MapReduce job that copies the data from one instance to another. Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd–

- (Exam Topic 6)
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster’s local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

1. A. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
2. B. Mount the Hive tables locally.
3. C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
4. D. Mount the Hive tables locally.
5. E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
6. F. Then run the Hadoop utility to copy them do HDF
7. G. Mount the Hive tables from HDFS.
8. H. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
9. I. Replicate external Hive tables to the native ones.
10. J. Load the ORC files into BigQuer
11. K. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
12. L. Replicate external Hive tables to the native ones.

Correct Answer: BC

- (Exam Topic 1)
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

1. A. Redefine the schema by evenly distributing reads and writes across the row space of the table.
2. B. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
3. C. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
4. D. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Correct Answer: A

- (Exam Topic 6)
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

1. A. Batch job, PubSubIO, side-inputs
2. B. Streaming job, PubSubIO, JdbcIO, side-outputs
3. C. Streaming job, PubSubIO, BigQueryIO, side-inputs
4. D. Streaming job, PubSubIO, BigQueryIO, side-outputs

Correct Answer: C

- (Exam Topic 5)
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

1. A. Both batch and streaming
2. B. BigQuery cannot be used as a sink
3. C. Only batch
4. D. Only streaming

Correct Answer: A
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts
Reference: https://cloud.google.com/dataflow/model/bigquery-io