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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
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
          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
                  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. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
                        • 2. Designing scalable and cost-effective data models
                          Building and operationalizing data processing systems24%- Data ingestion and integration
                          • 1. Streaming ingestion (Pub/Sub, Dataflow)
                            • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
                              - Data processing and transformation
                              • 1. Using Dataproc, Dataflow, and BigQuery SQL
                                • 2. ETL/ELT pipeline design

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q326-Q331):

                                  NEW QUESTION # 326
                                  Your company stores vital operational sales data in a BigQuery dataset in us-central1. Your company requires a disaster recovery plan to restore this data to us-east1 with a recovery point objective (RPO) of 24 hours and a recovery time objective (RTO) of 4 hours if us-central1 experiences an outage. You need to implement the disaster recovery plan while keeping costs and complexity to a minimum. What should you do?

                                  Answer: C

                                  Explanation:
                                  Manually exporting data daily to a multi-regional Cloud Storage bucket and restoring it with bq load meets the 24-hour RPO and allows recovery within a few hours, keeping both costs and operational complexity low. Continuous streaming or replication would increase complexity and cost unnecessarily for this RPO/RTO requirement.


                                  NEW QUESTION # 327
                                  You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?

                                  Answer: A

                                  Explanation:
                                  This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. Reference:
                                  Exporting table data | BigQuery | Google Cloud
                                  Storage classes | Cloud Storage | Google Cloud
                                  Retention policies and retention periods | Cloud Storage | Google Cloud Federated queries | BigQuery | Google Cloud


                                  NEW QUESTION # 328
                                  Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

                                  Answer: D

                                  Explanation:
                                  Explanation
                                  Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
                                  Reference: https://cloud.google.com/dataproc/docs/


                                  NEW QUESTION # 329
                                  You are using BigQuery's ML.GENERATE_TEXT function to write marketing materials for a new product launch. The problem is, the AI is generating random text that does not always relate to the product. You want the simplest way to improve the output to generate a marketing copy. What should you do?

                                  Answer: B

                                  Explanation:
                                  The temperature parameter is the primary control for randomness and "creativity" in Large Language Models (LLMs) used within BigQuery ML.
                                  * Randomness vs. Focus: A high temperature (e.g., closer to 1.0) leads to more random, diverse, and sometimes irrelevant output because the model is more likely to choose lower-probability tokens. If the AI is generating "random text," lowering the temperature (e.g., to 0.2 or 0.1) makes the model more deterministic and focused on the most likely next tokens related to the input prompt.
                                  * Simplest Way: Adjusting a single parameter in a SQL function is the "simplest" approach compared to gathering datasets for few-shot prompting (A) or the high complexity and cost of fine-tuning (C).
                                  * temperature vs. top_p: While top_p (nucleus sampling) also affects randomness, temperature is the standard first-line control for the overall "entropy" of the model's responses. Official Google documentation often suggests adjusting temperature first to curb hallucination or excessive randomness.
                                  Reference: Google Cloud Documentation on ML.GENERATE_TEXT:
                                  "temperature: A value in the range [0.0, 1.0]... It controls the degree of randomness in token selection.
                                  Lower temperature values are good for prompts that require a more deterministic and less open-ended response, while higher temperature values can lead to more diverse or creative results." (Source: ML.
                                  GENERATE_TEXT arguments)
                                  "To get more predictable responses from the model, use a lower temperature." (Source: BigQuery ML generative AI overview)


                                  NEW QUESTION # 330
                                  Which of these statements about exporting data from BigQuery is false?

                                  Answer: A

                                  Explanation:
                                  Explanation
                                  Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
                                  Reference: https://cloud.google.com/bigquery/docs/exporting-data


                                  NEW QUESTION # 331
                                  ......

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