優秀的Google GCP-DE考試大綱是行業領先材料&高品質的GCP-DE:Data Engineer

VCESoft的經驗豐富的專家團隊開發出了針對Google GCP-DE 認證考試的有效的培訓計畫,很適合參加Google GCP-DE 認證考試的考生。VCESoft為你提供的都是高品質的產品,可以讓你參加Google GCP-DE 認證考試之前做模擬考試,可以為你參加考試做最好的準備。
Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Building and operationalizing data processing systems | 30% | - Data pipeline implementation
- 1. Dataproc and Spark-based processing
- 2. Dataflow pipeline development
- Data ingestion and transformation
- 1. ETL/ELT workflows
- 2. Pub/Sub streaming ingestion
|
| Topic 2: Designing data processing systems | 20% | - Data pipeline architecture design
- 1. Scalable data ingestion design
- 2. Batch vs streaming data processing selection
- Storage and data modeling
- 1. Data lake architecture on Google Cloud Storage
- 2. Data warehouse design using BigQuery
|
| Topic 3: Operationalizing data and ML pipelines | 30% | - Pipeline automation and orchestration
- 1. Scheduling and monitoring pipelines
- 2. Cloud Composer workflows
- Monitoring and troubleshooting
- 1. Logging and observability
- 2. Performance optimization and debugging
|
| Topic 4: Maintaining and optimizing data and ML solutions | 20% | - Security and governance
- 1. IAM and access control
- 2. Data encryption and compliance
- Machine learning integration
- 1. Vertex AI integration for pipelines
- 2. BigQuery ML usage
|
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最新的 Google Cloud Certified GCP-DE 免費考試真題 (Q31-Q36):
問題 #31
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. Use at least 300 GB of data.
- B. Run your test for at least 10 minutes.
- C. Do not use a production instance.
- D. Before you test, run a heavy pre-test for several minutes.
答案:C
解題說明:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance
問題 #32
Which of these sources can you not load data into BigQuery from?
- A. Google Cloud SQL
- B. Google Drive
- C. File upload
- D. Google Cloud Storage
答案:A
解題說明:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data
問題 #33
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 CPU size on your server.
- B. Increase the size of the Google Persistent Disk on your server.
- C. Increase your network bandwidth from your datacenter to GCP.
- D. Increase your network bandwidth from Compute Engine to Cloud Storage.
答案:C
問題 #34
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?
- A. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
- B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
- C. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
- D. Load the data every 30 minutes into a new partitioned table in BigQuery.
- E. Store the data in a file in a regional Google Cloud Storage bucke
- F. Store the data in Google Cloud Datastor
答案:D
問題 #35
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?
- A. Cloud Bigtable
- B. Cloud SQL
- C. Cloud Spanner
- D. Cloud Datastore
答案:C
問題 #36
......
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