GCP-DE유효한덤프공부덤프공부자료

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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Building and operationalizing data processing systems | 30% | - Data ingestion and transformation
- 1. ETL/ELT workflows
- 2. Pub/Sub streaming ingestion
- Data pipeline implementation
- 1. Dataproc and Spark-based processing
- 2. Dataflow pipeline development
|
| Operationalizing data and ML pipelines | 30% | - Pipeline automation and orchestration
- 1. Scheduling and monitoring pipelines
- 2. Cloud Composer workflows
- Monitoring and troubleshooting
- 1. Performance optimization and debugging
- 2. Logging and observability
|
| Designing data processing systems | 20% | - Storage and data modeling
- 1. Data lake architecture on Google Cloud Storage
- 2. Data warehouse design using BigQuery
- Data pipeline architecture design
- 1. Batch vs streaming data processing selection
- 2. Scalable data ingestion design
|
| Maintaining and optimizing data and ML solutions | 20% | - Security and governance
- 1. Data encryption and compliance
- 2. IAM and access control
- Machine learning integration
- 1. BigQuery ML usage
- 2. Vertex AI integration for pipelines
|
>> GCP-DE유효한 덤프공부 <<
Google GCP-DE참고자료 - GCP-DE퍼펙트 덤프자료
Google GCP-DE 시험자료를 찾고 계시나요? Itexamdump의Google GCP-DE덤프가 고객님께서 가장 찾고싶은 자료인것을 믿어의심치 않습니다. Google GCP-DE덤프에 있는 문제와 답만 기억하시면 시험을 쉽게 패스하여 자격증을 취득할수 있습니다. 시험불합격시 덤프비용 환불가능하기에 시험준비 고민없이 덤프를 빌려쓰는것이라고 생각하시면 됩니다.
최신 Google Cloud Certified GCP-DE 무료샘플문제 (Q45-Q50):
질문 # 45
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 an additional table with only the necessary columns.
- B. Create a view on the table to present to the virtualization tool.
- C. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
- D. Export the data into a Google Sheet for virtualization.
정답:B
질문 # 46
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 WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
- B. Use the WHERE_PARTITIONTIME pseudo column
- C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
- D. Use the TABLE_DATE_RANGE function
정답:D
설명:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap
질문 # 47
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 Spanner
- C. Cloud SQL
- D. Cloud Datastore
정답:B
질문 # 48
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .
- A. conditional node
- B. master node
- C. application node
- D. worker node
정답:B
설명:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
질문 # 49
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. Data extraction
- C. ParDo
- D. Sink API
정답:C
설명:
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
질문 # 50
......
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