Valid Google Professional-Data-Engineer Test Topics - Professional-Data-Engineer Authorized Certification

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

>> Valid Google Professional-Data-Engineer Test Topics <<

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Google Certified Professional Data Engineer Exam Sample Questions (Q221-Q226):

NEW QUESTION # 221
Your neural network model is taking days to train. You want to increase the training speed. What can you do?

Answer: A

Explanation:
Explanation/Reference:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-
9f5d1c6f407d


NEW QUESTION # 222
You are part of a healthcare organization where data is organized and managed by respective data owners in various storage services. As a result of this decentralized ecosystem, discovering and managing data has become difficult. You need to quickly identify and implement a cost- optimized solution to assist your organization with the following:
- Data management and discovery
- Data lineage tracking
- Data quality validation
How should you build the solution?

Answer: C


NEW QUESTION # 223
Your team has created several BigQuery curated datasets containing anonymized industry benchmark data.
You want to make these datasets easily discoverable and accessible for querying by external partner companies within their own Google Cloud projects. You need a secure and scalable solution. What should you do?

Answer: C

Explanation:
Analytics Hub (part of BigQuery sharing) is the enterprise-grade, Google-recommended way to share data across organizational boundaries.
* Discoverability: Analytics Hub provides a central portal where partners can search for and "subscribe" to datasets. Traditional IAM grants (A) do not provide a discovery interface.
* Scalability: You can publish a single "listing" and allow many partners to subscribe to it. Each subscriber gets a "linked dataset" in their project.
* Security & Governance: The publisher retains control. You can see who has subscribed and even enforce "data egress" controls to prevent partners from copying or exporting the data while still allowing them to query it.
* Correcting other options:
* A: This works for a few users but lacks the metadata, searchability, and formal "subscription" lifecycle of Analytics Hub.
* C: Authorized views are for internal security within a project/org and don't solve the cross-org discovery problem.
* D: Exporting data creates data silos, increases storage costs, and loses the real-time query benefits of BigQuery.
Reference: Google Cloud Documentation on Analytics Hub:
"Analytics Hub is a data exchange platform that lets you share data and insights at scale across organizational boundaries... Listings let you share data without replicating the shared data... Subscribers discover data through the exchange and create a linked dataset in their own project to query the data." (Source: Introduction to BigQuery sharing)


NEW QUESTION # 224
You have a streaming pipeline that ingests data from Pub/Sub in production. You need to update this streaming pipeline with improved business logic. You need to ensure that the updated pipeline reprocesses the previous two days of delivered Pub/Sub messages. What should you do?
Choose 2 answers

Answer: A,B

Explanation:
To update a streaming pipeline with improved business logic and reprocess the previous two days of delivered Pub/Sub messages, you should use Pub/Sub Seek with a timestamp and Pub/Sub Snapshot capture two days before the deployment. Pub/Sub Seek allows you to replay or purge messages in a subscription based on a time or a snapshot. Pub/Sub Snapshot allows you to capture the state of a subscription at a given point in time and replay messages from that point. By using these features, you can ensure that the updated pipeline can process the messages that were delivered in the past two days without losing any data. Reference:
Pub/Sub Seek
Pub/Sub Snapshot


NEW QUESTION # 225
You are designing BigQuery tables for large volumes of clickstream event data. Your data analyst team will most frequently query by specific event date ranges and filter by the user ID UUID. You want to optimize table structure for query cost and performance. What should you do?

Answer: A

Explanation:
Comprehensive and Detailed Explanation:
This question is about applying the two primary optimization techniques in BigQuery: partitioning and clustering.
Partitioning divides a table into smaller segments based on a date, timestamp, or integer column. When you filter a query on the partition column, BigQuery performs "partition pruning," meaning it only scans the data in the relevant partitions. Since queries frequently filter by "specific event date ranges," partitioning by the event date column is the ideal strategy to reduce the amount of data scanned, which lowers cost and improves performance.
Clustering sorts the data within each partition based on the values in one or more columns. When you filter on a clustered column, BigQuery can use the sorted order to avoid scanning all the data within the relevant partitions. Since queries also filter by user ID, clustering by the user ID column will further improve performance and can reduce costs for those queries.
Conclusion: Option A is the correct answer because it correctly applies both techniques according to best practices: partition on the range-filtered column (event date) and cluster on the point-lookup/high-cardinality filtered column (user ID).
Option B is incorrect because you cannot partition a BigQuery table by a string column like a UUID.
Option C is incorrect because ingestion-time partitioning is less precise. Partitioning directly on the event date column is more effective for queries that filter on the event date itself.
Option D is incorrect because while clustering is helpful, partitioning is the more critical optimization for date range queries and provides the biggest cost savings. The combination of both is optimal.
Reference (Google Cloud Documentation Concepts):
The Google Cloud documentation for "Introduction to partitioned tables" and "Introduction to clustered tables" provides clear guidance. The best practice is to partition by a date or timestamp column that is commonly used as a filter to prune data. It then recommends to cluster by columns that are frequently used in WHERE clauses for filtering or in JOIN clauses. The combination of partitioning and clustering is a powerful optimization strategy.


NEW QUESTION # 226
......

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