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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q347-Q352):

NEW QUESTION # 347
You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)

Answer: D,E

Explanation:
C -> Adding more nodes to a cluster (not replication) can improve the write performance
https://cloud.google.com/bigtable/docs/performance
D -> since Google recommends adding nodes when storage utilization is > 70%
https://cloud.google.com/bigtable/docs/modifying-instance#nodes


NEW QUESTION # 348
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?

Answer: C

Explanation:
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.
References:
* Introduction to BigLake tables
* Upgrade an external table to BigLake
* BigQuery storage API


NEW QUESTION # 349
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

Answer: B,C

Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference:
https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html


NEW QUESTION # 350
You are planning to use Cloud Storage as pad of your data lake solution. The Cloud Storage bucket will contain objects ingested from external systems. Each object will be ingested once, and the access patterns of individual objects will be random. You want to minimize the cost of storing and retrieving these objects. You want to ensure that any cost optimization efforts are transparent to the users and applications. What should you do?

Answer: A

Explanation:
To minimize the cost of storing and retrieving objects in a Cloud Storage bucket while ensuring that cost optimization efforts are transparent to the users and applications, enabling Autoclass is the best approach. Here's why:
Autoclass Feature:
Autoclass automatically transitions objects between different storage classes (Standard, Nearline, Coldline, and Archive) based on their access patterns.
It ensures that frequently accessed data is kept in lower-latency, higher-cost storage classes and infrequently accessed data is moved to higher-latency, lower-cost storage classes.
Cost Optimization:
Autoclass optimizes storage costs by automatically moving objects to the most cost-effective storage class based on actual usage patterns, without manual intervention.
This feature ensures that objects are stored in the most economical class appropriate for their access frequency, reducing storage costs over time.
Transparency to Users:
The transition of objects between storage classes is handled automatically by Cloud Storage, making the process transparent to users and applications.
Users and applications interact with the objects in the same way, regardless of the underlying storage class, ensuring seamless access.
Steps to Implement:
Create a Cloud Storage Bucket:
When creating a new Cloud Storage bucket, enable the Autoclass feature.
Configure Autoclass:
Autoclass configuration is typically a straightforward process in the Google Cloud Console, where you enable it during bucket creation.
Monitor and Adjust:
Monitor the storage and access patterns through the Google Cloud Console to ensure that Autoclass is optimizing costs as expected.
Reference:
Google Cloud Storage Autoclass
Optimizing Storage Costs with Autoclass


NEW QUESTION # 351
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.
You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

Answer: C

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/data-lifecycle-cloud-platform


NEW QUESTION # 352
......

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