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

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q343-Q348):

質問 # 343
You migrated a data backend for an application that serves 10 PB of historical product data for analytics.
Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

A. 1. Store the historical data in BigQuery for analytics.2. In a Cloud SQL table, store the last state of the product after every product change.3. Serve the last state data directly from Cloud SQL to the API.
B. 1. Store the historical data in BigQuery for analytics.2. Use a materialized view to precompute the last state of a product.3. Serve the last state data directly from BigQuery to the API.
C. 1. Store the products as a collection in Firestore with each product having a set of historical changes.2.
Use simple and compound queries for analytics.3. Serve the last state data directly from Firestore to the API.
D. 1. Store the historical data in Cloud SQL for analytics.2. In a separate table, store the last state of the product after every product change.3. Serve the last state data directly from Cloud SQL to the API.

正解：B

質問 # 344
You are building an ELT solution in BigQuery by using Dataform. You need to perform uniqueness and null value checks on your final tables. What should you do to efficiently integrate these checks into your pipeline?

A. Create Dataplex data quality tasks.
B. Write a Spark-based stored procedure.
C. Build Dataform assertions into your code
D. Build BigQuery user-defined functions (UDFs).

正解：C

解説：
Dataform assertions are data quality tests that find rows that violate one or more rules specified in the query.
If the query returns any rows, the assertion fails. Dataform runs assertions every time it updates your SQL workflow and alerts you if any assertions fail. You can create assertions for all Dataform table types: tables, incremental tables, views, and materialized views. You can add built-in assertions to the config block of a table, such as nonNull and rowConditions, or create manual assertions with SQLX for advanced use cases.
Dataform automatically creates views in BigQuery that contain the results of compiled assertion queries, which you can inspect to debug failing assertions. Dataform assertions are an efficient way to integrate data quality checks into your ELT solution in BigQuery by using Dataform. References: Test tables with assertions
|Dataform | Google Cloud, Test data quality with assertions | Dataform, Data quality tests and documenting datasets | Dataform, Data quality testing with SQL assertions | Dataform

質問 # 345
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create a table on Cloud Spanner, and insert and delete rows with the job information
B. Create a table on Cloud SQL, and insert and delete rows with the job information
C. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
D. Create an API using App Engine to receive and send messages to the applications

正解：C

解説：
Pubsub is used to transmit data in real time and scale automatically.

質問 # 346
What is the HBase Shell for Cloud Bigtable?

A. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.
B. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.
C. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.
D. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.

正解：C

解説：
Explanation
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.
Reference: https://cloud.google.com/bigtable/docs/installing-hbase-shell

質問 # 347
A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open. After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

A. Create a Memorystore instance with a high availability (HA) configuration
B. Write votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes
C. Write votes to a Pub Sub tope and have Cloud Functions subscribe to it and write voles to BigQuery

正解：B

解説：
D Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas

質問 # 348
......

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