最新更新的Google GCP-DE證照考試是行業領先材料＆頂級的GCP-DE：Data Engineer

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Control operational costs 2. Optimize storage and compute usage 3. Tune query and pipeline performance - Reliability and scalability 1. Design highly available systems 2. Implement disaster recovery strategies 3. Scale data workloads efficiently
Topic 2: 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. Design orchestration workflows 2. Optimize pipeline performance and cost 3. Build ETL and ELT pipelines
Topic 3: Ensuring solution quality	20%-25%	- Data quality management 1. Maintain metadata and lineage 2. Implement data governance 3. Validate and clean data - Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls
Topic 4: Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Manage logging and alerting 2. Monitor data pipelines and workloads 3. Implement fault tolerance and recovery - Building data processing systems 1. Implement streaming data systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement batch processing systems

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GCP-DE證照考試：Data Engineer考試最新發布|更新的GCP-DE最新試題

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最新的 Google Cloud Certified GCP-DE 免費考試真題 (Q38-Q43):

問題 #38
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. Assemble 1,000 files into a tape archive (TAR) fil
B. Introduce data compression for each file to increase the rate file of file transfer.
C. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
D. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
F. 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.

答案：C,D

問題 #39
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. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
B. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
C. Merge the transformed tables together with a SQL query
D. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
E. 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
F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

答案：F

問題 #40
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. Python using MapReduce
B. PigLatin using Pig
C. Java using MapReduce
D. HiveQL using Hive

答案：A

問題 #41
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 an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role

答案：D

問題 #42
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision API by providing custom labels as recognition hints.
B. Use Cloud Vision AutoML with the existing dataset.
C. Train your own image recognition model leveraging transfer learning techniques.
D. Use Cloud Vision AutoML, but reduce your dataset twice.

答案：B

問題 #43
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