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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Transforming and cleaning data
  • 2. Orchestrating data workflows
  • 3. Ingesting data from various sources
- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
Topic 2: Maintaining and automating data workloads18%- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
Topic 3: Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation
- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads
Topic 4: Operationalizing machine learning models20%- Preparing data for ML
  • 1. Handling structured and unstructured data
  • 2. Feature engineering and data preparation
- Deploying and maintaining ML models
  • 1. Optimizing model performance and cost
  • 2. Model serving and monitoring
Topic 5: Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Ensuring data privacy and compliance
  • 2. Implementing access control and data protection
- Designing for business requirements
  • 1. Designing for scalability and elasticity
  • 2. Selecting appropriate storage solutions
  • 3. Designing for reliability and fault tolerance

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q124-Q129):

質問 # 124
You are troubleshooting your Dataflow pipeline that processes data from Cloud Storage to BigQuery. You have discovered that the Dataflow worker nodes cannot communicate with one another Your networking team relies on Google Cloud network tags to define firewall rules You need to identify the issue while following Google-recommended networking security practices. What should you do?

正解：D

解説：
Dataflow worker nodes need to communicate with each other and with the Dataflow service on TCP ports
12345 and 12346. These ports are used for data shuffling and streaming engine communication. By default, Dataflow assigns a network tag called dataflow to the worker nodes, and creates a firewall rule that allows traffic on these ports for the dataflow network tag. However, if you use a custom network tag for your Dataflow pipeline, you need to create a firewall rule that allows traffic on these ports for your custom network tag. Otherwise, the worker nodes will not be able to communicate with each other and the Dataflow service, and the pipeline will fail.
Therefore, the best way to identify the issue is to determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag. If there is no such firewall rule, or if the firewall rule does not match the network tag used by your Dataflow pipeline, you need to create or update the firewall rule accordingly.
Option A is not a good solution, as determining whether your Dataflow pipeline has a custom network tag set does not tell you whether there is a firewall rule that allows traffic on the required ports for that network tag.
You need to check the firewall rule as well.
Option C is not a good solution, as determining whether your Dataflow pipeline is deployed with the external IP address option enabled does not tell you whether there is a firewall rule that allows traffic on the required ports for the Dataflow network tag. The external IP address option determines whether the worker nodes can access resources on the public internet, but it does not affect the internal communication between the worker nodes and the Dataflow service.
Option D is not a good solution, as determining whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers does not tell you whether the firewall rule applies to the Dataflow network tag. The firewall rule should be based on the network tag, not the subnet, as the network tag is more specific and secure. References: Dataflow network tags | Cloud Dataflow | Google Cloud, Dataflow firewall rules | Cloud Dataflow | Google Cloud, Dataflow network configuration | Cloud Dataflow | Google Cloud, Dataflow Streaming Engine | Cloud Dataflow | Google Cloud.


質問 # 125
Your startup has a web application that currently serves customers out of a single region in Asia.
You are targeting funding that will allow your startup to serve customers globally. Your current goal is to optimize for cost, and your post-funding goal is to optimize for global presence and performance. You must use a native JDBC driver. What should you do?

正解：A

解説：
When you create a Cloud Spanner instance, you must configure it as either regional (that is, all the resources are contained within a single Google Cloud region) or multi-region (that is, the resources span more than one region).
You can change the instance configuration to multi-regional (or global) at anytime.


質問 # 126
Your company wants to implement a Retrieval-Augmented Generation (RAG) system to allow employees to query an extensive knowledge base of internal documents, such as policy manuals and project reports. You need to prepare this unstructured text for embedding to be used in the RAG system. What should you do to ensure the system can retrieve the most relevant information?

正解：A

解説：
In a RAG system, the "Retrieval" step relies on finding documents that are semantically similar to a user's query. This is achieved through Embeddings.
* Semantic Meaning: Simple keyword search (Option D) fails when a user asks a question using different wording than the document. By converting text into high-dimensional numerical vectors (embeddings), the system can calculate mathematical "closeness" between a query and a document based on meaning rather than just shared characters.
* Vector Search: These vectors are typically stored in a vector database (like Vertex AI Vector Search or AlloyDB with pgvector). This allows the RAG system to retrieve the most relevant "chunks" of text to feed into the Large Language Model (LLM).
* Correcting other options:
* B: Relational databases are not optimized for unstructured text retrieval based on semantic intent.
* C: While DLP is a good security practice, it is a preprocessing step for safety, not a step that enables retrieval or preparation for a RAG system's core functionality.
* D: Keyword indexing is the "old" way of searching and is significantly less effective than semantic vector search for modern AI applications.
Reference: Google Cloud Documentation on RAG and Embeddings:
"Vector embeddings provide a way to convert textual information into numerical data... This transformation involves representing data as vectors in a high-dimensional space where similar items are placed closer together, enabling efficient computations and similarity comparisons... Vector databases store documents as embeddings in a high-dimensional space, allowing for fast and accurate retrieval based on semantic similarity." (Source: What is Retrieval-Augmented Generation?)


質問 # 127
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub
streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

正解：D


質問 # 128
You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

正解：B

解説：
A Reshuffle operation is a way to force Dataflow to split the pipeline into multiple stages, which can help isolate the performance of each step and identify bottlenecks. By monitoring the execution details in the Dataflow console, you can see the time, CPU, memory, and disk usage of each stage, as well as the number of elements and bytes processed. This can help you diagnose where the pipeline is slowing down and optimize it accordingly. Reference:
1: Reshuffling your data
2: Monitoring pipeline performance using the Dataflow monitoring interface
3: Optimizing pipeline performance


質問 # 129
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