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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
| 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. Protect sensitive data
- 2. Implement encryption and compliance controls
- 3. Manage IAM and access control
|
| Designing data processing systems | 22%-27% | - Designing for data ingestion
- 1. Design batch and streaming ingestion solutions
- 2. Select appropriate storage systems
- 3. Design scalable and reliable ingestion architecture
- Designing data pipelines
- 1. Build ETL and ELT pipelines
- 2. Design orchestration workflows
- 3. Optimize pipeline performance and cost
|
| Managing and optimizing solutions | 20%-25% | - Managing resources and costs
- 1. Tune query and pipeline performance
- 2. Optimize storage and compute usage
- 3. Control operational costs
- Reliability and scalability
- 1. Scale data workloads efficiently
- 2. Implement disaster recovery strategies
- 3. Design highly available systems
|
| 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. Implement batch processing systems
- 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
|
Google Data Engineer Sample Questions:
1. All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.
A) before
B) only if
C) after
D) once
2. If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?
A) Do not use a production instance.
B) Use at least 300 GB of data.
C) Run your test for at least 10 minutes.
D) Before you test, run a heavy pre-test for several minutes.
3. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.
A) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
B) Use BigQuery'ssupport for external data sources to query.
C) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
D) Preserve the structure of the data as much as possible.
E) Use BigQuery UPDATE to further reduce the size of the dataset.
F) Denormalize the data as must as possible.
4. 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) Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
B) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
C) Use Cloud Deployment Manager to automate access provision.
D) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
E) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
F) Introduce resource hierarchy to leverage access control policy inheritance.
5. By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?
A) Windows at every 10 minutes
B) Windows at every 1 minute
C) Single, Global Window
D) Windows at every 100 MB of data
Solutions:
Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: A,C | Question # 4 Answer: E,F | Question # 5 Answer: C |