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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ensuring solution quality28%- Reliability and performance
  • 1. Fault tolerance and recovery strategies
    • 2. Monitoring pipelines and workloads
      - Security and governance
      • 1. Data governance and compliance
        • 2. IAM and access control in GCP
          Topic 2: Operationalizing machine learning models26%- Model deployment and monitoring
          • 1. Model monitoring and drift detection
            • 2. Online vs batch prediction
              - ML pipeline integration
              • 1. Vertex AI pipeline deployment
                • 2. Feature engineering and feature stores
                  Topic 3: Building and operationalizing data processing systems24%- Data ingestion and integration
                  • 1. Batch ingestion pipelines (BigQuery, Cloud Storage)
                    • 2. Streaming ingestion (Pub/Sub, Dataflow)
                      - Data processing and transformation
                      • 1. ETL/ELT pipeline design
                        • 2. Using Dataproc, Dataflow, and BigQuery SQL
                          Topic 4: Designing data processing systems22%- Data architecture and storage design
                          • 1. Designing scalable and cost-effective data models
                            • 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                              - Batch and streaming data processing design
                              • 1. Latency, throughput, and consistency trade-offs
                                • 2. Event-driven vs batch architectures

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q314-Q319):

                                  NEW QUESTION # 314
                                  You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
                                  What should you do?

                                  Answer: A


                                  NEW QUESTION # 315
                                  If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

                                  Answer: D

                                  Explanation:
                                  Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
                                  Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
                                  Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.


                                  NEW QUESTION # 316
                                  Different teams in your organization store customer and performance data in BigOuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?

                                  Answer: C

                                  Explanation:
                                  To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here's why option C is the best choice:
                                  Analytics Hub:
                                  Analytics Hub allows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.
                                  This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.
                                  Data Publishing and Subscribing:
                                  Teams can publish datasets they control, allowing them to manage access and updates independently.
                                  Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.
                                  Minimized Operational Tasks and Costs:
                                  This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.
                                  By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.
                                  Steps to Implement:
                                  Set Up Analytics Hub:
                                  Enable Analytics Hub in your Google Cloud project.
                                  Provide training to teams on how to publish and subscribe to data exchanges.
                                  Publish Data:
                                  Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.
                                  Subscribe to Data:
                                  Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.
                                  Reference:
                                  Analytics Hub Documentation
                                  Publishing Data in Analytics Hub
                                  Subscribing to Data in Analytics Hub


                                  NEW QUESTION # 317
                                  You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

                                  Answer: A


                                  NEW QUESTION # 318
                                  You are designing a pipeline that publishes application events to a Pub/Sub topic. Although message ordering is not important, you need to be able to aggregate events across disjoint hourly intervals before loading the results to BigQuery for analysis. What technology should you use to process and load this data to BigQuery while ensuring that it will scale with large volumes of events?

                                  Answer: A

                                  Explanation:
                                  Explanation/Reference:


                                  NEW QUESTION # 319
                                  ......

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