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[Hardware] Professional-Data-Engineer Fragenkatalog, Professional-Data-Engineer Prüfungs

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【Hardware】 Professional-Data-Engineer Fragenkatalog, Professional-Data-Engineer Prüfungs

Posted at yesterday 10:37      View：8 | Replies：0        Print      Only Author   [Copy Link] 1#
BONUS!!! Laden Sie die vollständige Version der Fast2test Professional-Data-Engineer Prüfungsfragen kostenlos herunter: https://drive.google.com/open?id=1PwLgdz-8-6_50qJQumCedB94SBqeYI_c
Die Fragenpool zur Google Professional-Data-Engineer Zertifizierungsprüfung von Fast2test hat eine große Ähnlichkeit mit den realen Prüfungen. Sie können in unseren Fragenpool den realen Prüfungsfragen begegnen. Das zeigt die Fähigkeiten unseres Expertenteams. Nun sind viele IT-Fachleute ganz ambitioniert. Sie beteiligen sich an der Google Professional-Data-Engineer Zertifizierungsprüfung, um sich den Bedürfnissen des Marktes anzupassen und ihren Traum zu verwirklichen.
Die Google Professional-Data-Engineer-Zertifizierungsprüfung deckt eine breite Palette von Themen ab, einschließlich Datenverarbeitungssysteme, Datenmodellierung, Datenanalyse, Datenvisualisierung und maschinelles Lernen. Es erfordert ein starkes Verständnis der Produkte und Dienste von Google Cloud -Plattform wie BigQuery, DataFlow, DataProc und Pub/Sub. Die Prüfung testet auch die Fähigkeit, Lösungen zu entwerfen und zu implementieren, die skalierbar, effizient und sicher sind.
Die Google Professional-Data-Engineer-Zertifizierungsprüfung ist ein sehr anerkanntes Zertifizierungsprogramm, das das Wissen und die Fähigkeiten von Fachleuten im Bereich Data Engineering validiert. Diese Zertifizierung soll die Fähigkeit von Dateningenieuren demonstrieren, Datenverarbeitungssysteme zu entwerfen, zu erstellen und zu pflegen sowie diese für Leistung und Kosteneffizienz zu beheben und zu optimieren. Die Zertifizierungsprüfung deckt eine Reihe von Themen ab, einschließlich Datenverarbeitungssysteme, Datenspeicherung und -verwaltung, Datenanalyse sowie maschinelles Lernen sowie Sicherheit und Konformität.
Google Professional-Data-Engineer Prüfung Übungen und AntwortenDie Produkte von Fast2test werden den Kandidaten nicht nur helfen, die Google Professional-Data-Engineer Zertifizierrungsprüfung zu bestehen, sondern Ihnen auch einen einjährigen kostenlosen Update-Service bieten. Sie wird den Kunden die neuesten Google Professional-Data-Engineer Prüfungsmaterialien so schnell wie möglich liefern, so dass sich die Kunden über die Prüfungsinformationen zur Google Professional-Data-Engineer Zertifizierung informieren können. Deshalb ist Fast2test eine erstklassige Website. Außerdem ist der Service hier auch ausgezeichnet.
Die Google Professional-Data-Engineer-Prüfung ist eine Zertifizierungsprüfung, die von Google Cloud Platform für Datenfachleute angeboten wird, die ihre Expertise im Entwerfen, Erstellen und Verwalten von Datenverarbeitungssystemen auf der Google Cloud Platform unter Beweis stellen möchten. Es handelt sich um eine hoch geschätzte Zertifizierung in der Branche und ist besonders relevant für diejenigen, die mit Big Data arbeiten möchten. Die Prüfung testet das Wissen des Kandidaten über verschiedene Datenengineering-Tools und -Technologien und das Bestehen der Prüfung zeigt, dass der Kandidat die Fähigkeiten und Kenntnisse hat, um Datenlösungen auf der Google Cloud Platform zu entwerfen und zu implementieren.
Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q30-Q35):30. Frage
Your financial services company has a critical daily reconciliation process that involves several distinct steps:
fetching data from an external SFTP server, decrypting the files, loading them into Cloud Storage, and finally running a series of BigQuery SQL transformations. Each step has strict dependencies, and the entire process should notify you if not completed by 7:00 AM. Manual intervention for failures is costly and delays compliance reporting. You need a highly observable and robust solution that supports easy re-runs of individual steps if errors occur. What should you do?
  • A. Create a Cloud Composer DAG that includes a single BashOperator to execute a top-level shell script, which in turn calls individual scripts for each pipeline step. Upload the scripts to a Cloud Composer environment's DAGs folder, and configure it to run daily.
  • B. Develop a Cloud Composer DAG that includes a single PythonOperator to execute a Python script that runs each step sequentially, incorporating error handling and retries. Upload the scripts to a Cloud Composer environment's DAGs folder, and configure it to run daily.
  • C. Define a Cloud Composer DAG to orchestrate the SFTP fetch and decryption steps, and then use Cloud Scheduler to trigger a separate Dataflow job that handles the Cloud Storage load and BigQuery transformations and schedule to run daily.
  • D. Implement a Cloud Composer DAG, with each step defined as a separate task using appropriate Airflow operators, and schedule the DAG for daily execution.
Antwort: D
Begründung:
Cloud Composer (based on Apache Airflow) is designed for workflow orchestration where complex dependencies exist. To achieve high observability and robustness (specifically the ability to re-run individual steps), the workflow must be decomposed into granular tasks.
* Granularity and Re-runs: By defining each step (SFTP fetch, Decrypt, GCS Load, BQ Transform) as a separate task in a DAG, Airflow tracks the state of each individually. If the "BigQuery SQL" step fails, you can "Clear" only that specific task in the UI to re-run it without re-fetching or re-decrypting the data, saving time and costs.
* Observability: Each task has its own logs and status in the Airflow UI. Options B and C (Single Operator/Script) are "black boxes"-if they fail, Airflow only knows the entire script failed, making it difficult to pinpoint where and impossible to re-run just the failed portion.
* SLA and Notifications: Airflow has built-in sla_miss_callbacks and email_on_failure features. You can set an sla parameter of 7:00 AM to automatically trigger alerts if the process is lagging.
* Correcting other options: * A: Splitting the logic between Composer and a separate Scheduler
/Dataflow job breaks the end-to-end lineage and makes it harder to manage dependencies and global SLAs.
* B & C: As mentioned, using a single operator for multiple logic steps defeats the purpose of an orchestrator's monitoring and retry capabilities.
Reference: Google Cloud Documentation on Cloud Composer / Airflow:
"An Airflow DAG is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies... Breaking down your workflow into multiple tasks allows for: Individual retries (re-running only failed parts), Parallel execution, and Clearer monitoring in the Airflow web interface." (Source: Key Airflow Concepts)
"You can use the SLA (Service Level Agreement) feature in Airflow to track whether a task or DAG takes longer than expected to finish... If a task exceeds its SLA, Airflow can send an email alert or trigger a callback function." (Source: Airflow Documentation - SLAs)

31. Frage
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?
  • A. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
  • B. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
  • C. Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.
  • D. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
Antwort: C
Begründung:
https://cloud.google.com/blog/pr ... tations-in-bigquery

32. Frage
Your company is migrating its on-premises data warehousing solution to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply daily updates from transactional database sources Your company wants to use BigQuery to improve its handling of CDC and to optimize the performance of the data warehouse Source system changes must be available for query m near-real time using tog-based CDC streams You need to ensure that changes in the BigQuery reporting table are available with minimal latency and reduced overhead. What should you do? Choose 2 answers
  • A. Insert each new CDC record and corresponding operation type into a staging table in real time
  • B. Perform a DML INSERT UPDATE, or DELETE to replicate each CDC record in the reporting table m real time.
  • C. Insert each new CDC record and corresponding operation type into the reporting table in real time and use a materialized view to expose only the current version of each unique record.
  • D. Periodically DELETE outdated records from the reporting tablePeriodically use a DML MERGE to simultaneously perform DML INSERT. UPDATE, and DELETE operations in the reporting table
Antwort: C,D

33. Frage
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 dat
a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.
  • A. Use BigQuery UPDATE to further reduce the size of the dataset.
  • B. Preserve the structure of the data as much as possible.
  • C. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
  • D. Denormalize the data as must as possible.
  • E. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
Antwort: D,E

34. Frage
You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups:
* Data engineers, which require lull data lake access
* Analytic users, which require access to curated data
You need to assign access rights to these two groups. What should you do?
  • A. 1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake.2. Grant the dataplex.dataOwner to the analytic user group on the customer curated zone.
  • B. 1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectviewer role on Cloud Storage buckets to data engineers.2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.
  • C. 1. Grant the bigquery.dataownex role on BigQuery datasets and the storage.objectcreator role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users.
  • D. 1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake.2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone.
Antwort: D
Begründung:
When designing a data mesh on Google Cloud using Dataplex to manage data in BigQuery and Cloud Storage, it is essential to simplify data asset permissions while ensuring that each user group has the appropriate access levels. Here's why option A is the best choice:
Data Engineer Group:
Data engineers require full access to the data lake to manage and operate data assets comprehensively.
Granting the dataplex.dataOwner role to the data engineer group on the customer data lake ensures they have the necessary permissions to create, modify, and delete data assets within the lake.
Analytic User Group:
Analytic users need access to curated data but do not require full control over all data assets. Granting the dataplex.dataReader role to the analytic user group on the customer curated zone provides read-only access to the curated data, enabling them to analyze the data without the ability to modify or delete it.
Steps to Implement:
Grant Data Engineer Permissions:
Assign the dataplex.dataOwner role to the data engineer group on the customer data lake to ensure full access and management capabilities.
Grant Analytic User Permissions:
Assign the dataplex.dataReader role to the analytic user group on the customer curated zone to provide read- only access to curated data.
Reference Links:
Dataplex IAM Roles and Permissions
Managing Access in Dataplex

35. Frage
......
Professional-Data-Engineer Prüfungs: https://de.fast2test.com/Professional-Data-Engineer-premium-file.html
Laden Sie die neuesten Fast2test Professional-Data-Engineer PDF-Versionen von Prüfungsfragen kostenlos von Google Drive herunter: https://drive.google.com/open?id=1PwLgdz-8-6_50qJQumCedB94SBqeYI_c
https://www.pruefungfrage.de
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