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Google Professional-Data-Engineerリンクグローバル:響く認定するProfessional-Data-Engineer資格準備
Professional-Data-Engineer試験に合格することが、最高のキャリアの機会です。 関連する証明書の豊富な経験は、企業があなたの選択のために一連の専門的な空席を開くために重要です。 当社のウェブサイトのProfessional-Data-Engineer学習クイズバンクおよび教材は、選択したトピックに基づいて最新の質問と回答を検索します。 この選択は、あなたのキャリア全体の突破口となるので、Professional-Data-Engineerスタディガイドの高い品質と正確性に驚かされるでしょう。
Google Professional-Data-Engineer 試験は、Google Cloud Platform上でのデータ処理システムの設計、構築、管理に必要なスキルと知識を検証するGoogleが提供する認定です。この認定は、Google Cloud上でのデータソリューションの設計や管理に専門知識を持つデータのプロフェッショナルを対象としています。試験には、データ処理システムの設計、データストレージソリューションの実装、データ処理インフラストラクチャの管理、およびデータのセキュリティとコンプライアンスなど、さまざまなトピックが含まれます。
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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q66-Q71):
質問 # 66
Cloud Bigtable is Google's ______ Big Data database service.
- A. mySQL
- B. Relational
- C. SQL Server
- D. NoSQL
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 # 67
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
- B. Create an API using App Engine to receive and send messages to the applications
- C. Create a table on Cloud Spanner, and insert and delete rows with the job information
- D. Create a table on Cloud SQL, and insert and delete rows with the job information
正解：B
質問 # 68
Flowlogistic Case Study
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. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
- D. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
正解：D
質問 # 69
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.
- A. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
- B. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
- C. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
- D. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- E. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
正解：A、B
質問 # 70
You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?
- A. Create a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Google-managed encryption key.
- B. Create a new BigOuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old Bigauery table.
- C. Create a new BigOuory table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.
- D. Use Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.
正解：C
解説：
To use CMEK for BigQuery, you need to create a key ring and a key in Cloud KMS, and then specify the key resource name when creating or updating a BigQuery table. You cannot change the encryption type of an existing table, so you need to create a new table with CMEK and copy the data from the old table with Google-managed encryption key.
Reference:
Customer-managed Cloud KMS keys | BigQuery | Google Cloud
Creating and managing encryption keys | Cloud KMS Documentation | Google Cloud
質問 # 71
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