GCP-DE日本語対策 - Data Engineerに合格するための信頼できるサポート

偶然的なIT試験は常にあなたの勉強の目標になって、あなたの運命を変えるかもしれません。Googleの重要な認証科目として、GCP-DE試験に参加する人が多くなっています。我々の参考資料は試験の状況によって更新されています。それに、あなたは資料を購入したら、我々はGCP-DE資料の更新の第一時間であなたを知らせます。

Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs - Operationalizing workloads 1. Monitoring and troubleshooting pipelines 2. Orchestrating and scheduling jobs 3. Ensuring reliability and recoverability
Topic 2: 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. Designing for data transformation and enrichment 3. Defining architecture for batch and streaming processing
Topic 3: Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Topic 4: 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 5: Ingesting and processing data	25%	- Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows - Transforming data 1. Applying data processing logic 2. Managing schema evolution 3. Optimizing transformations for performance

>> GCP-DE日本語対策 <<

試験の準備方法-最高のGCP-DE日本語対策試験-認定するGCP-DE英語版

弊社JpshikenのGCP-DE試験準備では、学習習慣を身に付けるのに役立ちます。 GCP-DE学習教材を購入して使用すると、学習の良い習慣を身に付けることができます。さらに重要なことは、良い習慣は科学的な小道具の学習方法を見つけ、学習効率を高めるのに役立ちます。そして、短時間でGCP-DE試験に合格するのに役立ちます。弊社からGCP-DEテストガイドを急いで購入すると、多くのメリットが得られます。

Google Data Engineer 認定 GCP-DE 試験問題 (Q30-Q35):

質問 # 30
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

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

正解：B

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 # 31
What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Split table into multiple tables; 2) Use a partitioned table
B. 1) Use a partitioned table; 2) Join tables into one table
C. 1) Join tables into one table; 2) Use nested repeated fields
D. 1) Use nested repeated fields; 2) Use a partitioned table

正解：C

解説：
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data

質問 # 32
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

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

正解：C

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 # 33
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
B. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
E. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP

正解：D

質問 # 34
Which of the following is NOT one of the three main types of triggers that Dataflow supports?

A. Trigger based on element count
B. Trigger based on time
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 # 35
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このラインで優秀なエリートになりたい場合は、GCP-DE認定を取得する必要があります。したがって、資格試験の重要性を通してそれを確認できます。資格試験を通じてのみ、対応する資格証明書を取得しているため、関連作業に従事することができます。そのため、GCP-DEテストの急流は、比較的短期間で人々が資格試験に合格するための非常に重要なツールです。 GCP-DE学習ツールを選択すると、ユーザーが困難な点をすばやく分析し、GCP-DE試験に合格するのに役立ちます。

GCP-DE英語版: https://www.jpshiken.com/GCP-DE_shiken.html