最新-一番優秀なGCP-DEテスト対策書試験-試験の準備方法GCP-DE対応受験

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Design highly available systems 2. Scale data workloads efficiently 3. Implement disaster recovery strategies - Managing resources and costs 1. Control operational costs 2. Optimize storage and compute usage 3. Tune query and pipeline performance
Topic 2: Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Manage IAM and access control 3. Protect sensitive data - Data quality management 1. Validate and clean data 2. Maintain metadata and lineage 3. Implement data governance
Topic 3: Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Design batch and streaming ingestion solutions 3. Select appropriate storage systems - Designing data pipelines 1. Optimize pipeline performance and cost 2. Design orchestration workflows 3. Build ETL and ELT pipelines
Topic 4: Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Implement fault tolerance and recovery 2. Manage logging and alerting 3. Monitor data pipelines and workloads - Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement batch processing systems 3. Implement streaming data systems

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Google GCP-DE対応受験 & GCP-DE参考書

我々社はGoogle GCP-DE問題集をリリースされる以来、たくさんの好評を博しました。試験に合格したお客様は「GCP-DE問題集のオンライン版を利用して、模擬試験を繰り返して受けました。無事試験に合格しました。JPNTestから大変助かりました。」と感謝します。あなたの支持こそ我々は最も高品質のGoogle GCP-DE問題集を開発して努力します。

Google Data Engineer 認定 GCP-DE 試験問題 (Q48-Q53):

質問 # 48
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: date#data_pointColumn data: device_id
B. Rowkey: device_idColumn data: date, data_point
C. Rowkey: date#device_idColumn data: data_point
D. Rowkey: dateColumn data: device_id, data_point
E. Rowkey: data_pointColumn data: device_id, date

正解：E

質問 # 49
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. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
C. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
E. Use Cloud Deployment Manager to automate access provision.
F. Introduce resource hierarchy to leverage access control policy inheritance.

正解：C、F

質問 # 50
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A. OutputCriteria
B. Windows
C. Sessions
D. Triggers

正解：D

解説：
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri

質問 # 51
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Dataflow
B. BigQuery
C. Cloud Bigtable
D. Cloud Datastore

正解：B

解説：
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

質問 # 52
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
C. Add a SideInput that returns a Boolean if the element is corrupt.
D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

正解：A

質問 # 53
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