GCP-DE関連日本語内容 & GCP-DE資格問題集

GCP-DE認定試験はIT業界の新たなターニングポイントの一つです。試験に受かったら、あなたはIT業界のエリートになることができます。情報技術の進歩と普及につれて、GoogleのGCP-DE問題集と解答を提供するオンライン·リソースが何百現れています。その中で、PassTestが他のサイトをずっと先んじてとても人気があるのは、PassTestのGoogleのGCP-DE試験トレーニング資料が本当に人々に恩恵をもたらすことができて、速く自分の夢を実現することにヘルプを差し上げられますから。

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	24%	- 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 - Planning data solutions 1. Planning for data security and compliance 2. Designing for reliability, scalability, and efficiency 3. Selecting appropriate storage solutions
Topic 2: 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. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention
Topic 3: Ingesting and processing data	25%	- Building data pipelines 1. Implementing ingestion mechanisms 2. Developing batch and streaming workflows 3. Handling data quality and consistency - Transforming data 1. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance
Topic 4: 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. Feature engineering and data preparation 2. Supporting model training and serving
Topic 5: Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs - Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management

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Google Data Engineer 認定 GCP-DE 試験問題 (Q75-Q80):

質問 # 75
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
C. Create a cron schedule in Cloud Dataprep.
D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

正解：A

質問 # 76
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Add ", UNNEST(person)" before the WHERE clause.
B. Change "person" to "city.person".
C. Change "person" to "person.city".
D. Add ", UNNEST(city)" before the WHERE clause.

正解：A

解説：
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

質問 # 77
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. Before you test, run a heavy pre-test for several minutes.
C. Do not use a production instance.
D. Run your test for at least 10 minutes.

正解：C

解説：
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

質問 # 78
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Cloud Scheduler
B. cron
C. Cloud Composer
D. Workflow Templates on Cloud Dataproc

正解：D

質問 # 79
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

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

正解：D

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 # 80
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