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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

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

Answer: A

NEW QUESTION # 67
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Single, Global Window
B. Windows at every 100 MB of data
C. Windows at every 10 minutes
D. Windows at every 1 minute

Answer: A

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 68
Which is not a valid reason for poor Cloud Bigtable performance?

A. The workload isn't appropriate for Cloud Bigtable.
B. The table's schema is not designed correctly.
C. There are issues with the network connection.
D. The Cloud Bigtable cluster has too many nodes.

Answer: D

Explanation:
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 69
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 Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Answer: C

NEW QUESTION # 70
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Train your own image recognition model leveraging transfer learning techniques.
B. Use Cloud Vision AutoML, but reduce your dataset twice.
C. Use Cloud Vision API by providing custom labels as recognition hints.
D. Use Cloud Vision AutoML with the existing dataset.

Answer: D

NEW QUESTION # 71
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