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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q114-Q119):

質問 # 114
You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

正解：D


質問 # 115
You have a Standard Tier Memorystore for Redis instance deployed in a production environment. You need to simulate a Redis instance failover in the most accurate disaster recovery situation, and ensure that the failover has no impact on production data. What should you do?

正解：B

解説：
To simulate a Redis instance failover in a production-like environment without impacting production data, the best approach is to use a development environment. Here's why option D is the best choice:
* Standard Tier Memorystore for Redis:
* The Standard Tier provides high availability and automatic failover capabilities. It's suitable for testing failover scenarios in a controlled environment.
* Development Environment:
* Using a development environment ensures that any potential data loss or impact from the failover simulation does not affect production data, maintaining the integrity and availability of the production system.
* Limited-Data-Loss Mode:
* The limited-data-loss mode for manual failover ensures that data loss is minimized during the failover process, making it a realistic simulation of a production failover scenario.
Steps to Implement:
* Create a Development Environment:
* Set up a development environment with a Standard Tier Memorystore for Redis instance that mirrors the configuration of your production instance.
* Initiate Manual Failover:
* Initiate a manual failover using the limited-data-loss data protection mode to simulate a failover scenario:
gcloud redis instances failover INSTANCE_ID --data-protection-mode=limited-data-loss
* Verify Failover:
* Monitor and verify the failover process to ensure it behaves as expected, simulating the disaster recovery scenario accurately.
Reference Links:
* Memorystore for Redis Documentation
* Manual Failover in Memorystore


質問 # 116
What are two methods that can be used to denormalize tables in BigQuery?

正解：A

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 # 117
You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures.
What should you do?

正解：B

解説：
To create a robust and resilient SQL pipeline in BigQuery that handles retries and failure notifications, consider the following:
* BigQuery Scheduled Queries: This feature allows you to schedule recurring queries in BigQuery. It is a straightforward way to run SQL transformations on a regular basis without requiring extensive setup.
* Error Handling and Retries: While BigQuery Scheduled Queries can run at specified intervals, they don't natively support complex retry logic or failure notifications directly. This is where additional Google Cloud services like Pub/Sub and Cloud Functions come into play.
* Pub/Sub for Notifications: By configuring a BigQuery scheduled query to publish messages to a Pub
/Sub topic upon failure, you can create a decoupled and scalable notification system.
* Cloud Functions: Cloud Functions can subscribe to the Pub/Sub topic and implement logic to count consecutive failures. After detecting three consecutive failures, the Cloud Function can then send an email notification using a service like SendGrid or Gmail API.
Implementation Steps:
* Set up a BigQuery Scheduled Query:
* Create a scheduled query in BigQuery to run your SQL transformation every two hours.
* Configure the scheduled query to publish a notification to a Pub/Sub topic in case of a failure.
* Create a Pub/Sub Topic:
* Create a Pub/Sub topic that will receive messages from the scheduled query.
* Develop a Cloud Function:
* Write a Cloud Function that subscribes to the Pub/Sub topic.
* Implement logic in the Cloud Function to track failure messages. If three consecutive failure messages are detected, the function sends an email notification.
Reference Links:
* BigQuery Scheduled Queries
* Pub/Sub Documentation
* Cloud Functions Documentation
* SendGrid Email API
* Gmail API


質問 # 118
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's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

正解：E

解説：
Explanation/Reference:


質問 # 119
......

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