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Google Professional-Data-Engineer試験は、データ技術での作業経験があり、Google Cloud Platform上でデータソリューションを設計および実装する専門家のために設計されています。試験は、Google Cloud Storage、Google BigQuery、Google Cloud Dataflowなどのさまざまなデータエンジニアリングツールと技術に関する候補者の知識をテストするように設計されています。

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q117-Q122):

質問 # 117
Case Study 1 - Flowlogistic
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
* Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
* 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

• A. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
• B. Use the NOW () function in BigQuery to record the event's time.
• C. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
• D. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

正解：C

質問 # 118
Which of these statements about BigQuery caching is true?

• A. BigQuery caches query results for 48 hours.
• B. There is no charge for a query that retrieves its results from cache.
• C. Query results are cached even if you specify a destination table.
• D. By default, a query's results are not cached.

正解：B

解説：
When query results are retrieved from a cached results table, you are not charged for the query. BigQuery caches query results for 24 hours, not 48 hours. Query results are not cached if you specify a destination table. A query's results are always cached except under certain conditions, such as if you specify a destination table.
Reference: https://cloud.google.com/bigquery/querying-data#query-caching

質問 # 119
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a
data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and
accommodate input data volume that will vary in size with minimal manual intervention. What should you
do?

• A. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
  Configure the job to use non-default Compute Engine machine types when needed.
• B. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the
  default autoscaling setting for worker instances.
• C. Use Cloud Dataproc to run your transformations. Use the diagnosecommand to generate an
  operational output archive. Locate the bottleneck and adjust cluster resources.
• D. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the
  number of worker nodes in your cluster via the command line.

正解：C

質問 # 120
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

• A. BigQuery
• B. Cloud Datastore
• C. Cloud BigTable
• D. Cloud SQL

正解：C

解説：
ference: https://cloud.google.com/solutions/business-intelligence/

質問 # 121
You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?

• A. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.
• B. Use Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.
• C. Use Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.
• D. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

正解：D

解説：
Shared VPC requires that you designate a host project to which networks and subnetworks belong and a service project, which is attached to the host project. When Cloud Composer participates in a Shared VPC, the Cloud Composer environment is in the service project. Reference: https://cloud.google.com/composer/docs/how-to/managing/configuring-shared-vpc

質問 # 122
......

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