GCP-DEオンライン試験 & GCP-DEテスト資料

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Orchestrating and scheduling jobs 3. Ensuring reliability and recoverability - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Optimizing query performance 2. Preparing datasets for querying and reporting 3. Implementing data sharing and governance - Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation
Designing data processing systems	24%	- Planning data solutions 1. Designing for reliability, scalability, and efficiency 2. Planning for data security and compliance 3. Selecting appropriate storage solutions - 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
Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Managing data lifecycle and retention 2. Using data storage services appropriately 3. Designing data warehouses and data lakes
Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Handling data quality and consistency 3. Developing batch and streaming workflows - Transforming data 1. Optimizing transformations for performance 2. Managing schema evolution 3. Applying data processing logic

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Google Data Engineer 認定 GCP-DE 試験問題 (Q42-Q47):

質問 # 42
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on time
B. Trigger based on element count
C. Trigger based on element size in bytes
D. Trigger that is a combination of other triggers

正解：C

解説：
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers

質問 # 43
What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and loading data from a file
B. Storage, queries, and exporting data
C. Queries and streaming inserts
D. Storage, queries, and streaming inserts

正解：D

解説：
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

質問 # 44
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

正解：B

質問 # 45
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. Use Cloud Deployment Manager to automate access provision.
B. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
C. Introduce resource hierarchy to leverage access control policy inheritance.
D. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
E. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
F. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

正解：B、C

質問 # 46
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
D. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

正解：C

解説：
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

質問 # 47
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GCP-DEテスト資料: https://www.mogiexam.com/GCP-DE-exam.html