Professional-Data-Engineer Prüfungsfragen Prüfungsvorbereitungen, Professional-Data-Engineer Fragen und Antworten, Google Certified Professional Data Engineer Exam

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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. Streaming ingestion (Pub/Sub, Dataflow)
        • 2. Batch ingestion pipelines (BigQuery, Cloud Storage)
          Topic 2: 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 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: 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

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                                  Professional-Data-Engineer Vorbereitungsfragen & Professional-Data-Engineer Unterlage

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                                  Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q117-Q122):

                                  117. Frage
                                  Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow. What should you do?

                                  Antwort: A

                                  Begründung:
                                  Pub/Sub exactly-once delivery is a feature that guarantees that subscriptions do not receive duplicate deliveries of messages based on a Pub/Sub-defined unique message ID. This feature is only supported by the pull subscription type, which is what you are using in this scenario. By enabling exactly-once delivery, you can ensure that each order is processed only once by a sales agent, and that no order is lost or duplicated. This also simplifies your workflow, as you do not need to create a separate database or subscription to monitor the pending or processed messages. Reference:
                                  Exactly-once delivery | Cloud Pub/Sub Documentation
                                  Cloud Pub/Sub Exactly-once Delivery feature is now Generally Available (GA)


                                  118. Frage
                                  Your company currently runs a large on-premises cluster using Spark, Hive, and HDFS in a colocation facility. The cluster is designed to accommodate peak usage on the system; however, many jobs are batch in nature, and usage of the cluster fluctuates quite dramatically. Your company is eager to move to the cloud to reduce the overhead associated with on-premises infrastructure and maintenance and to benefit from the cost savings. They are also hoping to modernize their existing infrastructure to use more serverless offerings in order to take advantage of the cloud. Because of the timing of their contract renewal with the colocation facility, they have only 2 months for their initial migration. How would you recommend they approach their upcoming migration strategy so they can maximize their cost savings in the cloud while still executing the migration in time?

                                  Antwort: C

                                  Begründung:
                                  1. migrate job and infrastructure to dataproc on clound
                                  2. any data, move from hdfs on-premise to google cloud storage ( one of them is Hive) If you want to modernize Hive to Bigquery , you are need to move it into GCS(preceding step) first and load it into bigquery that is all.
                                  Reference:
                                  https://cloud.google.com/blog/products/data-analytics/apache-hive-to-bigquery
                                  https://cloud.google.com/architecture/hadoop/migrating-apache-spark-jobs-to-cloud-dataproc
                                  https://cloud.google.com/architecture/hadoop/hadoop-gcp-migration-data


                                  119. Frage
                                  Which methods can be used to reduce the number of rows processed by BigQuery?

                                  Antwort: A

                                  Begründung:
                                  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


                                  120. Frage
                                  Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

                                  Antwort: D

                                  Begründung:
                                  The dataflow.worker role provides the permissions necessary for a Compute Engine service
                                  account to execute work units for a Dataflow pipeline


                                  121. Frage
                                  You have two projects where you run BigQuery jobs:
                                  * One project runs production jobs that have strict completion time SLAs. These are high priority jobs that must have the required compute resources available when needed. These jobs generally never go below a 300 slot utilization, but occasionally spike up an additional 500 slots.
                                  * The other project is for users to run ad-hoc analytical queries. This project generally never uses more than 200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity.
                                  You need to ensure that both projects have the appropriate compute resources available. What should you do?

                                  Antwort: B

                                  Begründung:
                                  To ensure that both production jobs with strict SLAs and ad-hoc queries have appropriate compute resources available while adhering to cost efficiency, setting up separate reservations and billing models for each project is the best approach. Here's why option B is the best choice:
                                  Separate Reservations for SLA and Ad-hoc Projects:
                                  Creating two separate reservations allows for dedicated resource management tailored to the needs of each project.
                                  The production project requires guaranteed slots with the ability to scale up as needed, while the ad-hoc project benefits from on-demand billing based on data scanned.
                                  Enterprise Edition Reservation for SLA Project:
                                  Setting a baseline of 300 slots ensures that the SLA project has the minimum required resources.
                                  Enabling autoscaling up to 500 additional slots allows the project to handle occasional spikes in workload without compromising on SLAs.
                                  On-Demand Billing for Ad-hoc Project:
                                  Using on-demand billing for the ad-hoc project ensures cost efficiency, as users are billed based on the amount of data scanned rather than reserved slot capacity.
                                  This model suits the less predictable and often lower-utilization nature of ad-hoc queries.
                                  Steps to Implement:
                                  Set Up Enterprise Edition Reservation for SLA Project:
                                  Create a reservation with a baseline of 300 slots.
                                  Enable autoscaling to allow up to an additional 500 slots as needed.
                                  Configure On-Demand Billing for Ad-hoc Project:
                                  Ensure that the ad-hoc project is set up to use on-demand billing, which charges based on data scanned by the queries.
                                  Monitor and Adjust:
                                  Continuously monitor the usage and performance of both projects to ensure that the configurations meet the needs and make adjustments as necessary.
                                  Reference:
                                  BigQuery Slot Reservations
                                  BigQuery On-Demand Pricing


                                  122. Frage
                                  ......

                                  Viele Leute meinen, man braucht viel fachliche IT-Kenntnisse, um die schwierigen Google Professional-Data-Engineer IT-Zertifizierungsprüfung zu bestehen. Nur diejenigen, die umfassende IT-Kenntnisse besitzen, sind qualifiziert dazu, sich an der Google Professional-Data-Engineer Prüfung zu beteiligen. Jetzt gibt es viele Methoden, die Ihre unausreichenden Fachkenntnisse wettmachen. Sie können sogar mit weniger Zeit und Energie als die fachlich gutqualifizierten die Google Professional-Data-Engineer Prüfung auch bestehen. Wie es heißt, viele Wege führen nach Rom.

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