Free Access Google.Professional-Data-Engineer.v2022-02-11.q268 Practice Test (Page 46)

Question 221

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?

A.Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

B.Redefine the schema by evenly distributing reads and writes across the row space of the table.

C.Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

D.The performance issue should be resolved over time as the site of the BigDate cluster is increased.

Question 222

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?

A.An IoT gateway connected to Cloud Pub/Sub, with Cloud Dataflow to read and process the messages from Cloud Pub/Sub.

B.Cloud Dataflow connected to the Kafka cluster to scale the processing of incoming messages.

C.Edge TPUs as sensor devices for storing and transmitting the messages.

D.A Kafka cluster virtualized on Compute Engine in us-east with Cloud Load Balancing to connect to the devices around the world.

Question 223

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

A.Do not use a production instance.

B.Run your test for at least 10 minutes.

C.Before you test, run a heavy pre-test for several minutes.

D.Use at least 300 GB of data.

Question 224

Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

A.Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

B.Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

C.Put the data into Google Cloud Storage.

D.Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Question 225

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.)

A.Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDFS. Mount the Hive tables locally.

B.Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluster. Mount the Hive tables locally.

C.Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluster. Then run the Hadoop utility to copy them do HDFS. Mount the Hive tables from HDFS.

D.Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive tables.
Replicate external Hive tables to the native ones.

E.Load the ORC files into BigQuery. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive tables. Replicate external Hive tables to the native ones.

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