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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Ensuring solution quality28%- Reliability and performance
  • 1. Monitoring pipelines and workloads
    • 2. Fault tolerance and recovery strategies
      - Security and governance
      • 1. IAM and access control in GCP
        • 2. Data governance and compliance
          Operationalizing machine learning models26%- ML pipeline integration
          • 1. Feature engineering and feature stores
            • 2. Vertex AI pipeline deployment
              - Model deployment and monitoring
              • 1. Model monitoring and drift detection
                • 2. Online vs batch prediction
                  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
                          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

                                  >> Professional-Data-Engineer Lab Questions <<

                                  Google Professional-Data-Engineer Exam Pattern | New Professional-Data-Engineer Test Cost

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                                  Google Certified Professional Data Engineer Exam Sample Questions (Q104-Q109):

                                  NEW QUESTION # 104
                                  You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

                                  Answer: D


                                  NEW QUESTION # 105
                                  You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/ Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing data. Which Stackdriver alerts should you create?

                                  Answer: C

                                  Explanation:
                                  Increase in number of undelivered messages shows that the messages are not getting subscribed.


                                  NEW QUESTION # 106
                                  Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
                                  # Syntax error : Expected end of statement but got "-" at [4:11]
                                  SELECT age
                                  FROM
                                  bigquery-public-data.noaa_gsod.gsod
                                  WHERE
                                  age != 99
                                  AND_TABLE_SUFFIX = `1929'
                                  ORDER BY
                                  age DESC
                                  Which table name will make the SQL statement work correctly?

                                  Answer: D

                                  Explanation:
                                  It follows the correct wildcard syntax of enclosing the table name in backticks and including the * wildcard character.


                                  NEW QUESTION # 107
                                  You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

                                  Answer: B


                                  NEW QUESTION # 108
                                  You need to modernize your existing on-premises data strategy. Your organization currently uses:
                                  - Apache Hadoop clusters for processing multiple large data sets,
                                  including on-premises Hadoop Distributed File System (HDFS) for data
                                  replication.
                                  - Apache Airflow to orchestrate hundreds of ETL pipelines with
                                  thousands of job steps.
                                  You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

                                  Answer: D


                                  NEW QUESTION # 109
                                  ......

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