Professional-Data-Engineer合格体験記 & Professional-Data-Engineer試験番号

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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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)
          Topic 2: Ensuring solution quality28%- Security and governance
          • 1. IAM and access control in GCP
            • 2. Data governance and compliance
              - Reliability and performance
              • 1. Monitoring pipelines and workloads
                • 2. Fault tolerance and recovery strategies
                  Topic 3: Operationalizing machine learning models26%- ML pipeline integration
                  • 1. Vertex AI pipeline deployment
                    • 2. Feature engineering and feature stores
                      - Model deployment and monitoring
                      • 1. Online vs batch prediction
                        • 2. Model monitoring and drift detection
                          Topic 4: 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. Using Dataproc, Dataflow, and BigQuery SQL
                                • 2. ETL/ELT pipeline design

                                  >> Professional-Data-Engineer合格体験記 <<

                                  Professional-Data-Engineer試験の準備方法｜ユニークなProfessional-Data-Engineer合格体験記試験｜正確的なGoogle Certified Professional Data Engineer Exam試験番号

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                                  Google Certified Professional Data Engineer Exam 認定 Professional-Data-Engineer 試験問題 (Q204-Q209):

                                  質問 # 204
                                  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?

                                  正解：D

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


                                  質問 # 205
                                  You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements:
                                  * Each department should have access only to their data.
                                  * Each department will have one or more leads who need to be able to create and update tables and provide them to their team.
                                  * Each department has data analysts who need to be able to query but not modify data.
                                  How should you set access to the data in BigQuery?

                                  正解：A


                                  質問 # 206
                                  You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?

                                  正解：D

                                  解説：
                                  The key requirements for the windowing strategy are:
                                  A window groups data for a specific sensor.
                                  A window should contain data spanningat least30 minutes ("duration of more than 30 minutes" implies activity for this period).
                                  A window for a sensorendswhen no data has been received from that sensor for 15 minutes (this is a gap).
                                  This scenario perfectly describessession windows.
                                  Session Windows:Session windows group elements (per key, e.g., per sensor ID) that arrive within a certain
                                  "gap duration" of each other. A new session starts if data for a key arrives after the gap duration has passed since the last data point for that key.
                                  In this case, if data stops arriving for a sensor for 15 minutes, the current session for that sensor closes. This matches "the window ends when no data has been received for 15 minutes." The "duration of more than 30 minutes" requirement is a condition you would applyafterthe session window closes. You'd calculate the duration of the data within the closed session window and only compute the average if that session's duration (span of event times within it) exceeds 30 minutes. Session windows themselves don't have a fixed duration; their duration is determined by data activity and the gap.
                                  Let's analyze why other options are less suitable:
                                  A (Hopping windows with a 15-minute window, and a thirty-minute period):Hopping windows have a fixed size and a fixed period. They create overlapping windows. This doesn't align with the dynamic nature of sessions ending based on inactivity. A 30-minute period with a 15-minute window means windows like [0:00-
                                  0:15], [0:15-0:30], [0:30-0:45]. If activity is continuous, a 30-minute activity span would be covered, but the window closing is not based on a 15-minute gap of inactivity.
                                  B (Tumbling windows with a 15-minute window and a fifteen-minute .withAllowedLateness operator):
                                  Tumbling windows are fixed-size, non-overlapping windows. .withAllowedLateness deals with late data arriving for a window that has already passed its end time, not with defining the window based on activity gaps.
                                  C (Session windows with a 30-minute gap duration):This would mean a session ends only if there's a 30- minute gap of inactivity. The requirement is a 15-minute gap.
                                  Therefore, session windows with a 15-minute gap duration (Option D) correctly model the requirement for windows to close after 15 minutes of inactivity from a sensor. The subsequent filtering for sessions lasting more than 30 minutes is a downstream operation.
                                  Reference:
                                  Apache Beam Programming Guide > Windowing > Windowing functions > Session windows. "Session windowing assigns elements to windows that represent sessions of activity. A session window starts when the first element arrives for a key. If another element arrives for that key within the specified gap duration, that element is included in the existing session window. If an element arrives after the gap duration, a new session window starts for that element... Session windows are useful for data that is irregularly distributed with respect to time, such as user activity data." This directly matches the sensor data behavior: data arrives when noise is high, and a period of no data for 15 minutes should close the analysis window for that sensor.


                                  質問 # 207
                                  You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

                                  正解：D

                                  解説：
                                  Reference:
                                  https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-app


                                  質問 # 208
                                  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?

                                  正解：D

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


                                  質問 # 209
                                  ......

                                  数年間でのIT認定試験資料向けの研究分析によって、我々社はこの業界のリーダーにだんだんなっています。弊社のチームは開発される問題集はとても全面で、受験生をGoogle　Professional-Data-Engineer試験に合格するのを良く助けます。周知のように、Google　Professional-Data-Engineer資格認定があれば、IT業界での発展はより簡単になります。

                                  Professional-Data-Engineer試験番号: https://www.jpexam.com/Professional-Data-Engineer_exam.html

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