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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q346-Q351):

                                  NEW QUESTION # 346
                                  Which methods can be used to reduce the number of rows processed by BigQuery?

                                  Answer: D

                                  Explanation:
                                  If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day. If you use the LIMIT clause, BigQuery will still process the entire table.
                                  Reference: https://cloud.google.com/bigquery/docs/partitioned-tables


                                  NEW QUESTION # 347
                                  You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)

                                  Answer: B,C


                                  NEW QUESTION # 348
                                  In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.

                                  Answer: D

                                  Explanation:
                                  Explanation
                                  To connect to the web interfaces, it is recommended to use an SSH tunnel to create a secure connection to the master node.
                                  Reference:
                                  https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#connecting_to_the_web_interfaces


                                  NEW QUESTION # 349
                                  You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier, 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand.
                                  You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

                                  Answer: A


                                  NEW QUESTION # 350
                                  You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK). You need to share the dataset with a partner organization that does not have access to your CMEK. What should you do?

                                  Answer: C

                                  Explanation:
                                  If you want to share a BigQuery dataset that uses a customer-managed encryption key (CMEK) with a partner organization that does not have access to your CMEK, you cannot use an authorized view or provide them a copy of your CMEK, because these options would violate the security and privacy of your data. Instead, you can copy the tables you need to share to a dataset without CMEKs, and then create an Analytics Hub listing for this dataset. Analytics Hub is a service that allows you to securely share and discover data assets across your organization and with external partners. By creating an Analytics Hub listing, you can grant the partner organization access to the copied dataset without CMEKs, and also control the level of access and the duration of the sharing. Reference:
                                  Customer-managed Cloud KMS keys
                                  [Authorized views]
                                  [Analytics Hub overview]
                                  [Creating an Analytics Hub listing]


                                  NEW QUESTION # 351
                                  ......

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