GCP-DE Übungstest: Data Engineer & GCP-DE Braindumps Prüfung

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ingesting and processing data	25%	- Transforming data 1. Managing schema evolution 2. Optimizing transformations for performance 3. Applying data processing logic - Building data pipelines 1. Handling data quality and consistency 2. Implementing ingestion mechanisms 3. Developing batch and streaming workflows
Storing and managing data	20%	- Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies - Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention
Maintaining and automating data workloads	18%	- Automation and optimization 1. Optimizing resource usage and costs 2. Automating deployment and management - Operationalizing workloads 1. Ensuring reliability and recoverability 2. Monitoring and troubleshooting pipelines 3. Orchestrating and scheduling jobs
Preparing data for analysis and machine learning	13%	- Preparing data for ML 1. Supporting model training and serving 2. Feature engineering and data preparation - Enabling data analysis 1. Preparing datasets for querying and reporting 2. Implementing data sharing and governance 3. Optimizing query performance
Designing data processing systems	24%	- Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Designing for data transformation and enrichment 3. Planning for data migration and integration - Planning data solutions 1. Planning for data security and compliance 2. Designing for reliability, scalability, and efficiency 3. Selecting appropriate storage solutions

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Viele Kandidaten wissen einfach nicht, wie sie sich auf die Prüfung vorbereiten können und hilflos sind. Aber mit den Schulungsunterlagen zur Google GCP-DE Zertifizierungsprüfung von ZertSoft ist alles ganz anders geworden. Mit ihr können Sie sich ganz selbstsicher auf Ihre Prüfung vorbereiten. Sie haben kein Risiko, in der Prüfung durchzufallen, mehr zu tragen. Das ist nicht nur seelische Hilfe. Am wichitgsten ist es, dass Sie die Prüfung bestehen und eine glänzende Zukunft haben können.

Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q21-Q26):

21. Frage
Which of these sources can you not load data into BigQuery from?

A. File upload
B. Google Cloud SQL
C. Google Cloud Storage
D. Google Drive

Antwort: B

Begründung:
You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there.
Reference: https://cloud.google.com/bigquery/loading-data

22. Frage
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
B. Introduce data compression for each file to increase the rate file of file transfer.
C. Assemble 1,000 files into a tape archive (TAR) fil
D. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
E. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
F. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Antwort: A,E

23. Frage
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Keep your row key as long as the field permits
B. Keep your row key reasonably short
C. Keep the row keep as an 8 bit integer
D. Include multiple time series values within the row key

Antwort: B

Begründung:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

24. Frage
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A. VPN
B. HTTPS
C. SOCKS
D. HTTP

Antwort: C

Begründung:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

25. Frage
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
B. Dataflow pipelines can consume data from other Google Cloud services
C. Dataflow pipelines can be programmed in Java
D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

Antwort: A

Begründung:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

26. Frage
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