Google - Pass-Sure Examinations GCP-DE Actual Questions

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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which is not a valid reason for poor Cloud Bigtable performance?

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

Answer: C

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 # 25
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. Use Cloud Deployment Manager to automate access provision.
B. Introduce resource hierarchy to leverage access control policy inheritance.
C. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Answer: B,F

NEW QUESTION # 26
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
C. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
D. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Answer: A

NEW QUESTION # 27
You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A. BigQuery
B. Cloud Datastore
C. Cloud SQL for PostgreSQL
D. Cloud Bigtable

Answer: A

NEW QUESTION # 28
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create a table on Cloud SQL, and insert and delete rows with the job information
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud Spanner, and insert and delete rows with the job information
D. Create an API using App Engine to receive and send messages to the applications

Answer: B

NEW QUESTION # 29
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