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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Professional-Data-Engineer学習資料、Professional-Data-Engineerミシュレーション問題

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q118-Q123):

質問 # 118
You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

A. Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery. use the connection to the Cloud Data Loss Prevention {Cloud DLP} API to de-identify the necessary data.
B. Set up Datastream to replicate your on-premise data on BigQuery.
C. Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.
D. Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming.
batch processing, and Cloud DLP Select BigQuery as your data sink.

正解：D

解説：
To load on-premises data into BigQuery while masking sensitive data, we need a solution that offers flexibility for both streaming and batch processing, as well as data masking capabilities. Here's a detailed explanation of why option B is the best choice:
* Apache Beam and Dataflow:
* Apache Beam SDKprovides a unified programming model for both batch and stream data processing.
* Google Cloud Dataflowis a fully managed service for executing Apache Beam pipelines, offering scalability and ease of use.
* Customization for Different Use Cases:
* By using the Apache Beam SDK, you can write custom pipelines that can handle both streaming and batch processing within the same framework.
* This allows you to switch between streaming and batch modes based on your use case without changing the core logic of your data pipeline.
* Data Masking with Cloud DLP:
* Google Cloud Data Loss Prevention (DLP)API can be integrated into your Apache Beam pipeline to de-identify and mask sensitive data programmatically before loading it into BigQuery.
* This ensures that sensitive data is handled securely and complies with privacy requirements.
* Cost Efficiency:
* Using Dataflow can be cost-effective because it is a fully managed service, reducing the operational overhead associated with managing your own infrastructure.
* The pay-as-you-go model ensures you only pay for the resources you consume, which can help keep costs under control.
Implementation Steps:
* Set up Apache Beam Pipeline:
* Write a pipeline using the Apache Beam SDK for Python that reads data from your on-premises storage.
* Add transformations for data processing, including the integration with Cloud DLP for data masking.
* Configure Dataflow:
* Deploy the Apache Beam pipeline on Google Cloud Dataflow.
* Customize the pipeline options for both streaming and batch use cases.
* Load Data into BigQuery:
* Set BigQuery as the sink for your data in the Apache Beam pipeline.
* Ensure the processed and masked data is loaded into the appropriate BigQuery tables.
Reference Links:
* Apache Beam Documentation
* Google Cloud Dataflow Documentation
* Google Cloud DLP Documentation
* BigQuery Documentation

質問 # 119
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl. being able to reprocess all failing data).
What should you do?

A. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
B. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.
C. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
D. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.

正解：C

解説：
Topic 2, 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 # 120
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

A. Increase the number of nodes in the Cloud Bigtable cluster
B. Configure your Cloud Dataflow pipeline to use local execution
C. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
D. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
E. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

正解：A、C

解説：
If you need to change DataFlow pipeline, better using Combine than CoGroupByKey according Google recommendations:
Combine is orders of magnitude faster than GroupByKey because Dataflow knows how to parallelize a combine step. Combine allows Dataflow to distribute a key to multiple workers and process it in parallel.

質問 # 121
Cloud Dataproc charges you only for what you really use with _____ billing.

A. month-by-month
B. hour-by-hour
C. week-by-week
D. minute-by-minute

正解：D

解説：
One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.

質問 # 122
You are using Cloud Bigtable to persist and serve stock market data for each of the major indices. To serve the trading application, you need to access only the most recent stock prices that are streaming in How should you design your row key and tables to ensure that you can access the data with the most simple query?

A. For each index, have a separate table and use a reverse timestamp as the row key design
B. Create one unique table for all of the indices, and then use a reverse timestamp as the row key design.
C. Create one unique table for all of the indices, and then use the index and timestamp as the row key design
D. For each index, have a separate table and use a timestamp as the row key design

正解：C

質問 # 123
......

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