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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q75-Q80):

NEW QUESTION # 75
All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

Answer: D

Explanation:
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


NEW QUESTION # 76
Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Answer: A


NEW QUESTION # 77
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

Answer: B

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance


NEW QUESTION # 78
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Answer: F


NEW QUESTION # 79
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 # 80
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