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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Monitoring the data lake 2. Processing data 3. Managing the lake (data discovery, access, cost controls) - 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. Defining architecture to support data access patterns 2. Designing the data model 3. Mapping business requirements 4. Deciding the degree of data normalization
Topic 2: Maintaining and automating data workloads (~15% of the exam)	15%	- Automating data processes 1. Scheduling jobs 2. Workflow orchestration 3. Continuous integration and continuous deployment (CI/CD) - Monitoring data pipelines and data processes 1. Managing quotas and resource usage 2. Logging, monitoring, and troubleshooting - Designing for reliability and fidelity 1. Recovering from failures 2. Performing data quality and validation checks 3. Planning for monitoring and alerting
Topic 3: Ingesting and processing the data (~20% of the exam)	20%	- Performing security considerations 1. Identity and Access Management (IAM) 2. Auditing, privacy, and compliance 3. Data encryption - 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 4: Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Cost optimization 2. Cluster sizing and autoscaling 3. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) - Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements - Designing data pipelines 1. Data acquisition and import 2. Processing logic 3. Batch processing 4. Streaming (e.g., windowing, late arriving data) 5. Integrating with new data sources 6. AI data enrichment
Topic 5: 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

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Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q379-Q384):

質問 # 379
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
No interaction by the user on the site for 1 hour

Has added more than $30 worth of products to the basket Has not completed a

transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

A. Use a global window with a time based trigger with a delay of 60 minutes.
B. Use a sliding time window with a duration of 60 minutes.
C. Use a session window with a gap time duration of 60 minutes.
D. Use a fixed-time window with a duration of 60 minutes.

正解：A

質問 # 380
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

A. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
B. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
C. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
D. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.

正解：C

質問 # 381
You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries What should you do?

A. Upgrade the external table to a BigLake table Enable metadata caching for the table.
B. Change the storage class of the Hive partitioned data objects from Coldline to Standard.
C. Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.
D. Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

正解：A

解説：
BigLake is a Google Cloud service that allows you to query structured data in external data stores such as Cloud Storage, Amazon S3, and Azure Blob Storage with access delegation and governance. BigLake tables extend the capabilities of BigQuery to data lakes and enable a flexible, open lakehouse architecture. By upgrading an external table to a BigLake table, you can improve the performance of your queries by leveraging the BigQuery storage API, which supports data format conversion, predicate pushdown, column projection, and metadata caching. Metadata caching reduces the number of requests to the external data store and speeds up query execution. To upgrade an external table to a BigLake table, you can use the ALTER TABLE statement with the SET OPTIONS clause and specify the enable_metadata_caching option as true. For example:
SQL
ALTER TABLE hive_partitioned_data
SET OPTIONS (
enable_metadata_caching=true
);
AI-generated code. Review and use carefully. More info on FAQ.
Reference:
Introduction to BigLake tables
Upgrade an external table to BigLake
BigQuery storage API

質問 # 382
You want to archive data in Cloud Storage. Because some data is very sensitive, you want to use the "Trust No One" (TNO) approach to encrypt your data to prevent the cloud provider staff from decrypting your data. What should you do?

A. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in Cloud Memorystore as permanent storage of the secret.
B. Use gcloud kms keys createto create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key and unique additional authenticated data (AAD). Use gsutil cp to upload each encrypted file to the Cloud Storage bucket, and keep the AAD outside of Google Cloud.
C. Specify customer-supplied encryption key (CSEK) in the .botoconfiguration file. Use gsutil cpto upload each archival file to the Cloud Storage bucket. Save the CSEK in a different project that only the security team can access.
D. Use gcloud kms keys create to create a symmetric key. Then use gcloud kms encryptto encrypt each archival file with the key. Use gsutil cpto upload each encrypted file to the Cloud Storage bucket.
Manually destroy the key previously used for encryption, and rotate the key once.

正解：D

質問 # 383
The Development and External teams nave the project viewer Identity and Access Management (1AM) role m a folder named Visualization. You want the Development Team to be able to read data from both Cloud Storage and BigQuery, but the External Team should only be able to read data from BigQuery. What should you do?

A. Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range
B. Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project
C. Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level
D. Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

正解：D

質問 # 384
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