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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q73-Q78):

NEW QUESTION # 73
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. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
B. Create a service account and grant dataset access to that accoun
C. Use the service account's private key to access the dataset
D. Create groups for your users and give those groups access to the dataset
E. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
F. Create a dummy user and grant dataset access to that use

Answer: B

NEW QUESTION # 74
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A. Source API
B. Sink API
C. Data extraction
D. ParDo

Answer: D

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION # 75
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 # 76
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
B. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
D. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

Answer: C

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 77
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Answer: B

NEW QUESTION # 78
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