Professional-Data-Engineer資格関連題 & Professional-Data-Engineer資格認定試験

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Maintaining and automating data workloads (~15% of the exam)15%- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
- Designing for reliability and fidelity
  • 1. Performing data quality and validation checks
  • 2. Recovering from failures
  • 3. Planning for monitoring and alerting
- Automating data processes
  • 1. Continuous integration and continuous deployment (CI/CD)
  • 2. Workflow orchestration
  • 3. Scheduling jobs
Topic 2: Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
Topic 3: Ingesting and processing the data (~20% of the exam)20%- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Deploying and operationalizing the pipelines
  • 1. CI/CD for data pipelines
  • 2. Job automation and orchestration (Cloud Composer, Workflows)
- Performing security considerations
  • 1. Identity and Access Management (IAM)
  • 2. Auditing, privacy, and compliance
  • 3. Data encryption
Topic 4: Designing data processing systems (~30% of the exam)30%- Selecting appropriate storage technologies
  • 1. Mapping storage options to business requirements
  • 2. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
- Designing data processing resources
  • 1. Cost optimization
  • 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 3. Cluster sizing and autoscaling
- Designing data pipelines
  • 1. Processing logic
  • 2. Data acquisition and import
  • 3. Streaming (e.g., windowing, late arriving data)
  • 4. Integrating with new data sources
  • 5. AI data enrichment
  • 6. Batch processing
Topic 5: Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Analyzing data access patterns
  • 2. Planning for storage costs and performance
  • 3. Lifecycle management of data
- Planning for using a data warehouse
  • 1. Mapping business requirements
  • 2. Defining architecture to support data access patterns
  • 3. Deciding the degree of data normalization
  • 4. Designing the data model
- Using a data lake
  • 1. Processing data
  • 2. Monitoring the data lake
  • 3. Managing the lake (data discovery, access, cost controls)
- Designing for a data platform
  • 1. Building a federated governance model for distributed data systems
  • 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage

>> Professional-Data-Engineer資格関連題 <<

Professional-Data-Engineer資格認定試験、Professional-Data-Engineer基礎問題集

この人材があちこちいる社会で、多くのプレッシャーを感じませんか。学歴はどんなに高くても実力を代表できません。学歴はただ踏み台だけで、あなたの地位を確保できる礎は実力です。GoogleのProfessional-Data-Engineer認定試験は人気がある認証で、その認証を持ちたい人がたくさんいます。この試験に受かったら自分のキャリアを固定することができます。JPNTestのGoogleのProfessional-Data-Engineer試験トレーニング資料はとても良いトレーニングツールで、あなたが首尾よく試験に合格ことを助けられます。試験に合格したら、あなたは国際的に認可され、解雇される心配する必要はありません。

Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q233-Q238):

質問 # 233
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

正解：A


質問 # 234
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

正解：B、D

解説：
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes.
[https://cloud.google.com/bigtable/docs/schema-design]
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


質問 # 235
Your analytics team wants to build a simple statistical model to determine which customers are most likely to work with your company again, based on a few different metrics. They want to run the model on Apache Spark, using data housed in Google Cloud Storage, and you have recommended using Google Cloud Dataproc to execute this job. Testing has shown that this workload can run in approximately 30 minutes on a 15-node cluster, outputting the results into Google BigQuery. The plan is to run this workload weekly. How should you optimize the cluster for cost?

正解：D


質問 # 236
Cloud Bigtable is Google's ______ Big Data database service.

正解：D

解説：
Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail. It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.
Reference: https://cloud.google.com/bigtable/


質問 # 237
Your organization is modernizing their IT services and migrating to Google Cloud. You need to organize the data that will be stored in Cloud Storage and BigQuery. You need to enable a data mesh approach to share the data between sales, product design, and marketing departments What should you do?

正解：D

解説：
Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.
References:
Architecture and functions in a data mesh - Google Cloud
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Build a Data Mesh with Dataplex | Google Cloud Skills Boost


質問 # 238
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GoogleのProfessional-Data-Engineerの認定試験は当面いろいろな認証試験で最も価値がある試験の一つです。最近の数十年間で、コンピュータ科学の教育は世界各地の数多くの注目を得られています。GoogleのProfessional-Data-Engineerの認定試験はIT情報技術領域の欠くことができない一部ですから、IT領域の人々はこの試験認証に合格することを通じて自分自身の知識を増加して、他の分野で突破します。JPNTestのGoogleのProfessional-Data-Engineer認定試験の問題と解答はそういう人たちのニーズを答えるために研究した成果です。この試験に合格することがたやすいことではないですから、適切なショートカットを選択するのは成功することの必要です。JPNTestはあなたの成功を助けるために存在しているのですから、JPNTestを選ぶということは成功を選ぶのことと等しいです。JPNTestが提供した問題と解答はIT領域のエリートたちが研究と実践を通じて開発されて、十年間過ぎのIT認証経験を持っています。

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