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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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最新的 Google Cloud Certified Professional-Data-Engineer 免費考試真題 (Q405-Q410):

問題 #405
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. 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.
C. 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.
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.

答案：B

解題說明：
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.

問題 #406
Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

A. BETWEEN
B. WHERE
C. SELECT
D. LIMIT

答案：C

解題說明：
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.

問題 #407
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DT stores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRING type. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

A. Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.
B. Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.
C. Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.
D. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
E. Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

答案：B

解題說明：
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.

問題 #408
You are working on a sensitive project involving private user dat

A. Grant the consultant the Viewer role on the project.
B. Grant the consultant the Cloud Dataflow Developer role on the project.
C. Create a service account and allow the consultant to log on with it.
D. Create an anonymized sample of the data for the consultant to work with in a different project.
E. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?

答案：B

問題 #409
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

A. Disable external IP addresses and establish a Private Service Connect endpoint IP address.
B. Disable external IP addresses from worker VMs and enable Private Google Access.
C. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
D. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.

答案：B

解題說明：
Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs. References:
* Private Google Access overview
* Using Private Google Access and Cloud NAT
* Using custom containers with Dataflow

問題 #410
......

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