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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Managing and optimizing solutions20%-25%- Reliability and scalability
  • 1. Scale data workloads efficiently
  • 2. Design highly available systems
  • 3. Implement disaster recovery strategies
- Managing resources and costs
  • 1. Tune query and pipeline performance
  • 2. Control operational costs
  • 3. Optimize storage and compute usage
Topic 2: Building and operationalizing data processing systems28%-33%- Operationalizing systems
  • 1. Implement fault tolerance and recovery
  • 2. Monitor data pipelines and workloads
  • 3. Manage logging and alerting
- Building data processing systems
  • 1. Implement streaming data systems
  • 2. Implement batch processing systems
  • 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
Topic 3: Ensuring solution quality20%-25%- Data quality management
  • 1. Validate and clean data
  • 2. Maintain metadata and lineage
  • 3. Implement data governance
- Security and compliance
  • 1. Protect sensitive data
  • 2. Implement encryption and compliance controls
  • 3. Manage IAM and access control
Topic 4: Designing data processing systems22%-27%- Designing data pipelines
  • 1. Design orchestration workflows
  • 2. Optimize pipeline performance and cost
  • 3. Build ETL and ELT pipelines
- Designing for data ingestion
  • 1. Design scalable and reliable ingestion architecture
  • 2. Design batch and streaming ingestion solutions
  • 3. Select appropriate storage systems

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Google Data Engineer Sample Questions (Q52-Q57):

NEW QUESTION # 52
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

Answer: H,L


NEW QUESTION # 53
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Answer: A

Explanation:
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


NEW QUESTION # 54
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

Answer: C,E


NEW QUESTION # 55
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?

Answer: C


NEW QUESTION # 56
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: D,E


NEW QUESTION # 57
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