GCP-DE Valid Exam Duration - Sample GCP-DE Questions Answers

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	22%-27%	- Designing data pipelines 1. Design orchestration workflows 2. Optimize pipeline performance and cost 3. Build ETL and ELT pipelines - Designing for data ingestion 1. Design batch and streaming ingestion solutions 2. Design scalable and reliable ingestion architecture 3. Select appropriate storage systems
Topic 2: Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Implement batch processing systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement streaming data systems - Operationalizing systems 1. Implement fault tolerance and recovery 2. Monitor data pipelines and workloads 3. Manage logging and alerting
Topic 3: Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Design highly available systems 2. Implement disaster recovery strategies 3. Scale data workloads efficiently - Managing resources and costs 1. Tune query and pipeline performance 2. Optimize storage and compute usage 3. Control operational costs
Topic 4: Ensuring solution quality	20%-25%	- Data quality management 1. Implement data governance 2. Maintain metadata and lineage 3. Validate and clean data - Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls

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Google Data Engineer Sample Questions (Q51-Q56):

NEW QUESTION # 51
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create a cron schedule in Cloud Dataprep.
B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

Answer: B

NEW QUESTION # 52
What are the minimum permissions needed for a service account used with Google Dataproc?

A. Write to Google Cloud Storage; read to Google Cloud Logging
B. Execute to Google Cloud Storage; execute to Google Cloud Logging
C. Read and write to Google Cloud Storage; write to Google Cloud Logging
D. Execute to Google Cloud Storage; write to Google Cloud Logging

Answer: C

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION # 53
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be multi-regional
B. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
C. Set the BigQuery dataset to be regiona
D. In the event of an emergency, use a point-in-time snapshot to recover the data.
E. In the event of an emergency, use a point-in-time snapshot to recover the data.
F. Set the BigQuery dataset to be multi-regional
G. Set the BigQuery dataset to be regiona
H. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
I. In the event of an emergency, use the backup copy of the table.
J. In the event of an emergency, use the backup copy of the table.

Answer: E

NEW QUESTION # 54
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Answer: A

NEW QUESTION # 55
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. Replicate external Hive tables to the native ones.
B. Then run the Hadoop utility to copy them do HDF
C. Mount the Hive tables locally.
D. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
E. Replicate external Hive tables to the native ones.
F. Load the ORC files into BigQuer
G. Mount the Hive tables locally.
H. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
I. Mount the Hive tables from HDFS.
J. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
K. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
L. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF

Answer: D,G

NEW QUESTION # 56
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