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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q406-Q411):

質問 # 406
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?

• A. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
• B. Cloud Dataflow, Cloud SQL, and Cloud Storage
• C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
• D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
• E. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

正解：C

質問 # 407
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world.
The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco's Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

• A. The disk size per worker
• B. The maximum number of workers
• C. The zone
• D. The number of workers

正解：B

質問 # 408
Your neural network model is taking days to train. You want to increase the training speed. What can you do?

• A. Subsample your test dataset.
• B. Increase the number of layers in your neural network.
• C. Increase the number of input features to your model.
• D. Subsample your training dataset.

正解：B

解説：
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d

質問 # 409
You migrated your on-premises Apache Hadoop Distributed File System (HDFS) data lake to Cloud Storage.
The data scientist team needs to process the data by using Apache Spark and SQL. Security policies need to be enforced at the column level. You need a cost-effective solution that can scale into a data mesh. What should you do?

• A. 1. Apply an Identity and Access Management (IAM) policy at the file level in Cloud Storage.
  2. Define a BigQuery external table for SQL processing.
  3. Use Dataproc Spark to process the Cloud Storage files.
• B. 1. Deploy a long-living Dataproc cluster with Apache Hive and Ranger enabled.
  2. Configure Ranger for column level security.
  3. Process with Dataproc Spark or Hive SQL.
• C. 1. Define a BigLake table.
  2. Create a taxonomy of policy tags in Data Catalog.
  3. Add policy tags to columns.
  4. Process with the Spark-BigQuery connector or BigQuery SQL.
• D. 1. Load the data to BigQuery tables.
  2. Create a taxonomy of policy tags in Data Catalog.
  3. Add policy tags to columns.
  4. Process with the Spark-BigQuery connector or BigQuery SQL.

正解：C

解説：
The key requirements are:
Data on Cloud Storage (migrated from HDFS).
Processing with Spark and SQL.
Column-level security.
Cost-effective and scalable for a data mesh.
Let's analyze the options:
Option A (Load to BigQuery tables, policy tags, Spark-BQ connector/BQ SQL):
Pros: BigQuery native tables offer excellent performance. Policy tags provide robust column-level security managed centrally in Data Catalog. The Spark-BigQuery connector allows Spark to read from/write to BigQuery. BigQuery SQL is powerful. Scales well.
Cons: "Loading" the data into BigQuery means moving it from Cloud Storage into BigQuery's managed storage. This incurs storage costs in BigQuery and an ETL step. While effective, it might not be the most
"cost-effective" if the goal is to query data in place on Cloud Storage, especially for very large datasets.
Option B (Long-living Dataproc, Hive, Ranger):
Pros: Provides a Hadoop-like environment with Spark, Hive, and Ranger for column-level security.
Cons: "Long-living Dataproc cluster" is generally not the most cost-effective, as you pay for the cluster even when idle. Managing Hive and Ranger adds operational overhead. While scalable, it requires more infrastructure management than serverless options.
Option C (IAM at file level, BQ external table, Dataproc Spark):
Pros: Using Cloud Storage is cost-effective for storage. BigQuery external tables allow SQL access.
Cons: IAM at the file level in Cloud Storage does not provide column-level security. This option fails to meet a critical requirement.
Option D (Define a BigLake table, policy tags, Spark-BQ connector/BQ SQL):
Pros:BigLake Tables: These tables allow you to query data in open formats (like Parquet, ORC) on Cloud Storage as if it were a native BigQuery table, but without ingesting the data into BigQuery's managed storage.
This is highly cost-effective for storage.
Column-Level Security with Policy Tags: BigLake tables integrate with Data Catalog policy tags to enforce fine-grained column-level security on the data residing in Cloud Storage. This is a centralized and robust security model.
Spark and SQL Access: Data scientists can use BigQuery SQL directly on BigLake tables. The Spark- BigQuery connector can also be used to access BigLake tables, enabling Spark processing.
Cost-Effective & Scalable Data Mesh: This approach leverages the cost-effectiveness of Cloud Storage, the serverless querying power and security features of BigQuery/Data Catalog, and provides a clear path to building a data mesh by allowing different domains to manage their data in Cloud Storage while exposing it securely through BigLake.
Cons: Performance for BigLake tables might be slightly different than BigQuery native storage for some workloads, but it's designed for high performance on open formats.
Why D is superior for this scenario:
BigLake tables (Option D) directly address the need to keep data in Cloud Storage (cost-effective for a data lake) while providing strong, centrally managed column-level security via policy tags and enabling both SQL (BigQuery) and Spark (via Spark-BigQuery connector) access. This is more aligned with modern data lakehouse and data mesh architectures than loading everything into native BigQuery storage (Option A) if the data is already in open formats on Cloud Storage, or managing a full Hadoop stack on Dataproc (Option B).
Reference:
Google Cloud Documentation: BigLake > Overview. "BigLake lets you unify your data warehouses and data lakes. BigLake tables provide fine-grained access control for tables based on data in Cloud Storage, while preserving access through other Google Cloud services like BigQuery, GoogleSQL, Spark, Trino, and TensorFlow." Google Cloud Documentation: BigLake > Introduction to BigLake tables. "BigLake tables bring BigQuery features to your data in Cloud Storage. You can query external data with fine-grained security (including row- level and column-level security) without needing to move or duplicate data." Google Cloud Documentation: Data Catalog > Overview of policy tags. "You can use policy tags to enforce column-level access control for BigQuery tables, including BigLake tables." Google Cloud Blog: "Announcing BigLake - Unifying data lakes and warehouses" (and similar articles) highlight how BigLake enables querying data in place on Cloud Storage with BigQuery's governance features.

質問 # 410
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

• A. Train on the new data while using the existing data as your test set.
• B. Continuously retrain the model on a combination of existing data and the new data.
• C. Continuously retrain the model on just the new data.
• D. Train on the existing data while using the new data as your test set.

正解：D

解説：
https://cloud.google.com/automl-tables/docs/prepare
Topic 1, 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
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.

質問 # 411
......

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