Google Professional-Data-Engineer Questions - Free Professional-Data-Engineer Dumps For Every Exam [2026]

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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q307-Q312):

                                  NEW QUESTION # 307
                                  Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

                                  Answer: B

                                  Explanation:
                                  https://towardsdatascience.com/understanding-hyperparameters-and-its-optimisation-techniques-f0debba07568


                                  NEW QUESTION # 308
                                  You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

                                  Answer: A

                                  Explanation:
                                  To store your data in a way that meets both access patterns, you should:
                                  * A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day. This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery's features such as partitioning, clustering, and caching.
                                  * B. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key. This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs.
                                  BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness. Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.
                                  * C. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day. This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.
                                  * D. Store your data in BigQuery. Use the metric as a primary key. This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.


                                  NEW QUESTION # 309
                                  Your financial services company is moving to cloud technology and wants to store 50 TB of financial time-series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?

                                  Answer: C

                                  Explanation:
                                  Explanation/Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series


                                  NEW QUESTION # 310
                                  The marketing team at your organization provides regular updates of a segment of your customer dataset.
                                  The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

                                  Answer: B

                                  Explanation:
                                  https://cloud.google.com/blog/products/gcp/performing-large-scale-mutations-in-bigquery


                                  NEW QUESTION # 311
                                  Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

                                  Answer: B

                                  Explanation:
                                  The dataflow.worker role provides the permissions necessary for a Compute Engine service account to execute work units for a Dataflow pipeline Reference:
                                  https://cloud.google.com/dataflow/access-control


                                  NEW QUESTION # 312
                                  ......

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