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| Section | Weight | Objectives |
|---|
| Storing and managing data | 20% | - Optimizing storage performance and cost
- 1. Implementing cost optimization strategies
- 2. Configuring storage for access patterns
- Implementing storage solutions
- 1. Managing data lifecycle and retention
- 2. Using data storage services appropriately
- 3. Designing data warehouses and data lakes
|
| Maintaining and automating data workloads | 18% | - Automation and optimization
- 1. Automating deployment and management
- 2. Optimizing resource usage and costs
- Operationalizing workloads
- 1. Monitoring and troubleshooting pipelines
- 2. Orchestrating and scheduling jobs
- 3. Ensuring reliability and recoverability
|
| Designing data processing systems | 24% | - Designing data pipelines
- 1. Defining architecture for batch and streaming processing
- 2. Planning for data migration and integration
- 3. Designing for data transformation and enrichment
- Planning data solutions
- 1. Designing for reliability, scalability, and efficiency
- 2. Selecting appropriate storage solutions
- 3. Planning for data security and compliance
|
| 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. Optimizing query performance
- 3. Implementing data sharing and governance
|
| Ingesting and processing data | 25% | - Building data pipelines
- 1. Developing batch and streaming workflows
- 2. Implementing ingestion mechanisms
- 3. Handling data quality and consistency
- Transforming data
- 1. Applying data processing logic
- 2. Optimizing transformations for performance
- 3. Managing schema evolution
|
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Google Data Engineer Sample Questions (Q67-Q72):
NEW QUESTION # 67
Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?
- A. Increase the size of the Google Persistent Disk on your server.
- B. Increase the CPU size on your server.
- C. Increase your network bandwidth from your datacenter to GCP.
- D. Increase your network bandwidth from Compute Engine to Cloud Storage.
Answer: C
NEW QUESTION # 68
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?
- A. Use the TABLE_DATE_RANGE function
- B. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- C. Use the WHERE_PARTITIONTIME pseudo column
- D. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
Answer: A
Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap
NEW QUESTION # 69
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. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
- B. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
- C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
- D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
Answer: D
NEW QUESTION # 70
Which is not a valid reason for poor Cloud Bigtable performance?
- A. There are issues with the network connection.
- B. The table's schema is not designed correctly.
- 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 # 71
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?
- A. Create a view on the table to present to the virtualization tool.
- B. Export the data into a Google Sheet for virtualization.
- C. Create an additional table with only the necessary columns.
- D. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
Answer: A
NEW QUESTION # 72
......
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