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NEW QUESTION 47
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

  • A. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
  • B. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
  • C. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
  • D. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.

Answer: D

 

NEW QUESTION 48
Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them.
Due to the dynamic nature of the campaign, the data is growing exponentially every hour. The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named(“ReadLogData”)
.from(“clouddataflow-readonly:samples.log_data”)
You want to improve the performance of this data read. What should you do?

  • A. Specify the Tableobject in the code.
  • B. Call a transform that returns TableRow objects, where each element in the PCollexction represents a single row in the table.
  • C. Use of both the Google BigQuery TableSchema and TableFieldSchema classes.
  • D. Use .fromQuery operation to read specific fields from the table.

Answer: D

Explanation:
BigQueryIO.read.from() directly reads the whole table from BigQuery. This function exports the whole table to temporary files in Google Cloud Storage, where it will later be read from. This requires almost no computation, as it only performs an export job, and later Dataflow reads from GCS (not from BigQuery).
BigQueryIO.read.fromQuery() executes a query and then reads the results received after the query execution. Therefore, this function is more time-consuming, given that it requires that a query is first executed (which will incur in the corresponding economic and computational costs).

 

NEW QUESTION 49
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

  • A. Preemptible workers cannot use persistent disk.
  • B. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
  • C. A Dataproc cluster cannot have only preemptible workers.
  • D. Preemptible workers cannot store data.

Answer: C,D

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
. Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
. No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
. Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.

 

NEW QUESTION 50
……

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