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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Storing and managing data | 20% | - Implementing storage solutions
- 1. Using data storage services appropriately
- 2. Designing data warehouses and data lakes
- 3. Managing data lifecycle and retention
- Optimizing storage performance and cost
- 1. Implementing cost optimization strategies
- 2. Configuring storage for access patterns
|
| Topic 2: Maintaining and automating data workloads | 18% | - Operationalizing workloads
- 1. Ensuring reliability and recoverability
- 2. Monitoring and troubleshooting pipelines
- 3. Orchestrating and scheduling jobs
- Automation and optimization
- 1. Automating deployment and management
- 2. Optimizing resource usage and costs
|
| Topic 3: Preparing data for analysis and machine learning | 13% | - Preparing data for ML
- 1. Feature engineering and data preparation
- 2. Supporting model training and serving
- Enabling data analysis
- 1. Preparing datasets for querying and reporting
- 2. Implementing data sharing and governance
- 3. Optimizing query performance
|
| Topic 4: Designing data processing systems | 24% | - Designing data pipelines
- 1. Planning for data migration and integration
- 2. Defining architecture for batch and streaming processing
- 3. Designing for data transformation and enrichment
- Planning data solutions
- 1. Selecting appropriate storage solutions
- 2. Planning for data security and compliance
- 3. Designing for reliability, scalability, and efficiency
|
| Topic 5: Ingesting and processing data | 25% | - Building data pipelines
- 1. Handling data quality and consistency
- 2. Implementing ingestion mechanisms
- 3. Developing batch and streaming workflows
- Transforming data
- 1. Managing schema evolution
- 2. Optimizing transformations for performance
- 3. Applying data processing logic
|
Google Data Engineer Sample Questions:
1. You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?
A) Cloud Functions
B) Cloud Scheduler
C) Cloud Composer
D) Cloud Dataflow
2. 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) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
B) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
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) Introduce resource hierarchy to leverage access control policy inheritance.
E) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
F) Use Cloud Deployment Manager to automate access provision.
3. What is the general recommendation when designing your row keys for a Cloud Bigtable schema?
A) Keep the row keep as an 8 bit integer
B) Keep your row key reasonably short
C) Keep your row key as long as the field permits
D) Include multiple time series values within the row key
4. What are the minimum permissions needed for a service account used with Google Dataproc?
A) Read and write to Google Cloud Storage; write to Google Cloud Logging
B) Execute to Google Cloud Storage; write to Google Cloud Logging
C) Execute to Google Cloud Storage; execute to Google Cloud Logging
D) Write to Google Cloud Storage; read to Google Cloud Logging
5. 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) Cloud SQL for PostgreSQL
B) BigQuery
C) Cloud Datastore
D) Cloud Bigtable
Solutions:
Question # 1 Answer: B | Question # 2 Answer: A,D | Question # 3 Answer: B | Question # 4 Answer: A | Question # 5 Answer: B |