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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues
Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance - Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance
Building and operationalizing data processing systems	25%	- Building data pipelines 1. Ingesting data from various sources 2. Transforming and cleaning data 3. Orchestrating data workflows - Deploying and managing systems 1. Monitoring and logging data processes 2. Managing infrastructure and resources
Operationalizing machine learning models	20%	- Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation - Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring
Maintaining and automating data workloads	18%	- Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates - Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q57-Q62):

57. Frage
You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

A. Update the current pipeline and use the drain flag.
B. Update the current pipeline and provide the transform mapping JSON object.
C. Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.
D. Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.

Antwort: C

58. Frage
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
C. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
D. Add capacity (memory and disk space) to the database server by the order of 200.

Antwort: C

59. Frage
You have one BigQuery dataset which includes customers' street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?

A. Create a de-identification job in Cloud Data Loss Prevention and use the masking transformation.
B. Write a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word "street" appears.
C. Create a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.
D. Create a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.

Antwort: D

Begründung:
To retrieve all occurrences of street addresses from a BigQuery dataset, the most effective and comprehensive method is to use Cloud Data Loss Prevention (DLP). Here's why option A is the best choice:
* Cloud Data Loss Prevention (DLP):
* Cloud DLP is designed to discover, classify, and protect sensitive information. It includes pre- defined infoTypes for various kinds of sensitive data, including street addresses.
* Using Cloud DLP ensures thorough and accurate detection of street addresses based on advanced pattern recognition and contextual analysis.
* Deep Inspection Job:
* A deep inspection job allows you to scan entire tables for sensitive information.
* By creating an inspection template that includes the STREET_ADDRESS infoType, you can ensure that all instances of street addresses are detected across your dataset.
* Scalability and Accuracy:
* Cloud DLP is scalable and can handle large datasets efficiently.
* It provides a high level of accuracy in identifying sensitive data, reducing the risk of missing any occurrences.
Steps to Implement:
* Set Up Cloud DLP:
* Enable the Cloud DLP API in your Google Cloud project.
* Create an Inspection Template:
* Create an inspection template in Cloud DLP that includes the STREET_ADDRESS infoType.
* Run Deep Inspection Jobs:
* Create and run a deep inspection job for each table in your dataset using the inspection template.
* Review the inspection job results to retrieve all occurrences of street addresses.
Reference Links:
* Cloud DLP Documentation
* Creating Inspection Jobs

60. Frage
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Antwort: A

61. Frage
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?

A. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.
B. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
C. Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.
D. Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.

Antwort: B

62. Frage
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Die Google Professional-Data-Engineer Zertifizierungsprüfung ist eine der wertvollsten zeitgenössischen Zertifizierungsprüfung. In den letzten Jahrzehnten ist die Computer-Ausbildung schon ein Fokus geworden. Sie ist ein notwendiger Bestandteil der Informations-Technologie im IT-Bereich. So legen viele IT-fachleute diese Prüfung ab, um ihr Wissen zu erweitern und einen Durchbruch in allen Bereichen zu verschaffen. Und unsere Fragen und Antworten zur Google Professional-Data-Engineer Zertifizierungsprüfung sind genau das, was sie brauchen. Dennoch ist es schwer, diesen Test zu bestehen. Wählen Sie die entsprechende Abkürzung, um Erfolg zu garantieren. Wählen Sie Pass4Test, kommt der Erfolg auf Sie zu. Die Fragen und Antworten zur Google Professional-Data-Engineer Zerzifizierung von Pass4Test werden von den IT-Eliten nach ihren Erfahrungen und Praxien bearbeitet und haben die Zertifizierungserfahrung von mehr als zehn Jahren.

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