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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Manage logging and alerting 2. Monitor data pipelines and workloads 3. Implement fault tolerance and recovery - Building data processing systems 1. Implement streaming data systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement batch processing systems
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
Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Scale data workloads efficiently 2. Implement disaster recovery strategies 3. Design highly available systems - Managing resources and costs 1. Optimize storage and compute usage 2. Tune query and pipeline performance 3. Control operational costs
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. Implement encryption and compliance controls 2. Protect sensitive data 3. Manage IAM and access control

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Google Data Engineer Sample Questions (Q73-Q78):

NEW QUESTION # 73
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A. Cloud Datastore
B. Cloud SQL
C. Cloud Bigtable
D. Cloud Spanner

Answer: B

NEW QUESTION # 74
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

A. An alert based on a decrease of instance/storage/used_bytes for the source and a rate of change increase of subscription/num_undelivered_messages for the destination
B. An alert based on an increase of subscription/num_undelivered_messages for the source and a rate of change decrease of instance/storage/used_bytes for the destination
C. An alert based on an increase of instance/storage/used_bytes for the source and a rate of change decrease of subscription/num_undelivered_messages for the destination
D. An alert based on a decrease of subscription/num_undelivered_messages for the source and a rate of change increase of instance/storage/used_bytes for the destination

Answer: B

NEW QUESTION # 75
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 Dataflow, and Cloud Storage
B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Answer: B

NEW QUESTION # 76
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Create a service account and grant dataset access to that accoun
B. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
C. Create a dummy user and grant dataset access to that use
D. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
E. Use the service account's private key to access the dataset
F. Create groups for your users and give those groups access to the dataset

Answer: A

NEW QUESTION # 77
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. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
C. Create a cron schedule in Cloud Dataprep.
D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Answer: B

NEW QUESTION # 78
......

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