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[General] Reliable Google Professional-Data-Engineer Test Sample | Exam Professional-Data-

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【General】 Reliable Google Professional-Data-Engineer Test Sample | Exam Professional-Data-

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Google Certified Professional Data Engineer Exam Sample Questions (Q115-Q120):NEW QUESTION # 115
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. Use .fromQueryoperation to read specific fields from the table.
  • B. Call a transform that returns TableRowobjects, where each element in the PCollectionrepresents a single row in the table.
  • C. Use of both the Google BigQuery TableSchemaand TableFieldSchemaclasses.
  • D. Specify the TableReferenceobject in the code.
Answer: B

NEW QUESTION # 116
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences.
Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.
  • A. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
  • B. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
  • C. Use Cloud Deployment Manager to automate access provision.
  • D. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
  • E. Introduce resource hierarchy to leverage access control policy inheritance.
Answer: C,D
Explanation:
Explanation/Reference:

NEW QUESTION # 117
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. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
  • B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below
    4000, send an alert.
  • C. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below
    4000, send an alert.
  • D. Consume the stream of data in Cloud Dataflow using Kafka IO. 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.
Answer: B

NEW QUESTION # 118
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
  • A. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
  • B. Preemptible workers cannot store data.
  • C. Preemptible workers cannot use persistent disk.
  • D. A Dataproc cluster cannot have only preemptible workers.
Answer: B,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.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 119
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 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.
  • 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 encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
  • 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: C
Explanation:
https://cloud.google.com/compute ... -managed-encryption

NEW QUESTION # 120
......
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