GCP-DE Übungsmaterialien & GCP-DE Lernführung: Data Engineer & GCP-DE Lernguide

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	20%-25%	- Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls - Data quality management 1. Implement data governance 2. Maintain metadata and lineage 3. Validate and clean data
Designing data processing systems	22%-27%	- Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Design batch and streaming ingestion solutions 3. Select appropriate storage systems - Designing data pipelines 1. Optimize pipeline performance and cost 2. Build ETL and ELT pipelines 3. Design orchestration workflows
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Tune query and pipeline performance 2. Optimize storage and compute usage 3. Control operational costs - Reliability and scalability 1. Scale data workloads efficiently 2. Implement disaster recovery strategies 3. Design highly available systems
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Monitor data pipelines and workloads 2. Manage logging and alerting 3. Implement fault tolerance and recovery - Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement batch processing systems 3. Implement streaming data systems

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GCP-DE Deutsche Prüfungsfragen, GCP-DE Zertifikatsfragen

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Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q22-Q27):

22. Frage
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

A. once
B. before
C. after
D. only if

Antwort: B

Begründung:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

23. Frage
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create an API using App Engine to receive and send messages to the applications
B. Create a table on Cloud Spanner, and insert and delete rows with the job information
C. Create a table on Cloud SQL, and insert and delete rows with the job information
D. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them

Antwort: D

24. Frage
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data has invalid rows that were skipped on import.
B. The CSV data loaded in BigQuery is not flagged as CSV.
C. The CSV data has not gone through an ETL phase before loading into BigQuery.
D. The CSV data loaded in BigQuery is not using BigQuery's default encoding.

Antwort: A

25. Frage
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Merge the transformed tables together with a SQL query
B. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
C. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
E. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

Antwort: F

26. Frage
You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A. Use a standard persistent disk and 50% preemptible worker
B. Deploy a Cloud Dataproc cluste
C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
E. Change references in scripts from hdfs:// to gs://
F. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
G. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
H. Deploy a Cloud Dataproc cluste
I. Use an SSD persistent disk and 50% preemptible worker
J. Install the Cloud Storage connector, and store the data in Cloud Storag
K. Change references in scripts from hdfs:// to gs://

Antwort: H

27. Frage
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GCP-DE Deutsche Prüfungsfragen: https://www.zertsoft.com/GCP-DE-pruefungsfragen.html