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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Ingesting and processing data25%- Transforming data
  • 1. Applying data processing logic
  • 2. Managing schema evolution
  • 3. Optimizing transformations for performance
- Building data pipelines
  • 1. Handling data quality and consistency
  • 2. Developing batch and streaming workflows
  • 3. Implementing ingestion mechanisms
Storing and managing data20%- Implementing storage solutions
  • 1. Using data storage services appropriately
  • 2. Managing data lifecycle and retention
  • 3. Designing data warehouses and data lakes
- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Preparing datasets for querying and reporting
  • 2. Optimizing query performance
  • 3. Implementing data sharing and governance
- Preparing data for ML
  • 1. Supporting model training and serving
  • 2. Feature engineering and data preparation
Maintaining and automating data workloads18%- 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
Designing data processing systems24%- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Selecting appropriate storage solutions
  • 3. Designing for reliability, scalability, and efficiency
- Designing data pipelines
  • 1. Designing for data transformation and enrichment
  • 2. Planning for data migration and integration
  • 3. Defining architecture for batch and streaming processing

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Google Data Engineer Sample Questions (Q74-Q79):

NEW QUESTION # 74
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

Answer: A


NEW QUESTION # 75
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.)

Answer: A,F


NEW QUESTION # 76
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.

Answer: A,C


NEW QUESTION # 77
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

Answer: D


NEW QUESTION # 78
Cloud Dataproc is a managed Apache Hadoop and Apache service.

Answer: D

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/


NEW QUESTION # 79
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