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2023 Google Professional-Data-Engineer: Google Certified Professional Data Engineer Exam –Professional Valid Braindumps Questions

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Understanding functional and technical aspects of Google Professional Data Engineer Exam Building and operationalizing data processing systems

The following will be discussed here:

• Batch and streaming
• Data cleansing
• Monitoring pipelines
• Building and operationalizing data processing systems
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)
• Transformation
• Building and operationalizing storage systems
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Validating a migration
• Integrating with new data sources

Google Certified Professional Data Engineer Exam Sample Questions (Q258-Q263):

NEW QUESTION # 258
Scaling a Cloud Dataproc cluster typically involves ____.

• A. increasing or decreasing the number of master nodes
• B. deleting applications from unused nodes periodically
• C. increasing or decreasing the number of worker nodes
• D. moving memory to run more applications on a single node

Answer: C Explanation:
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1 ) increase the number of workers to make a job run faster
2 ) decrease the number of workers to save money
3 ) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage Reference: https://cloud.google.com/dataproc/docs/concepts/scaling-clusters
NEW QUESTION # 259
Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

• A. dataflow.compute
• B. dataflow.worker
• C. dataflow.developer
• D. dataflow.viewer

Answer: B Explanation:
The dataflow.worker role provides the permissions necessary for a Compute Engine service
account to execute work units for a Dataflow pipeline
NEW QUESTION # 260
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

• A. Pipeline
• B. Transform
• C. PCollection
• D. Sink API

Answer: B Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model
NEW QUESTION # 261
You are designing a basket abandonment system for an ecommerce company. The system will send a
message to a user based on these rules:
No interaction by the user on the site for 1 hour

Has added more than $30 worth of products to the basket

Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should
you design the pipeline?

• A. Use a global window with a time based trigger with a delay of 60 minutes.
• B. Use a sliding time window with a duration of 60 minutes.
• C. Use a session window with a gap time duration of 60 minutes.
• D. Use a fixed-time window with a duration of 60 minutes.

Answer: A
NEW QUESTION # 262
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

• A. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
• B. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
• C. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
• D. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
• E. Create a Google Cloud Dataflow job to process the data.

Answer: D Explanation:
Dataproc is used to migrate Hadoop and Spark jobs on GCP. Dataproc with GCS connected through Google Cloud Storage connector helps store data after the life of the cluster. When the job is high I/O intensive, then we need to create a small persistent disk.
NEW QUESTION # 263
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