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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Protect sensitive data 3. Manage IAM and access control - Data quality management 1. Validate and clean data 2. Implement data governance 3. Maintain metadata and lineage
Designing data processing systems	22%-27%	- Designing data pipelines 1. Build ETL and ELT pipelines 2. Optimize pipeline performance and cost 3. Design orchestration workflows - Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Select appropriate storage systems 3. Design batch and streaming ingestion solutions
Managing and optimizing solutions	20%-25%	- Reliability and scalability 1. Implement disaster recovery strategies 2. Design highly available systems 3. Scale data workloads efficiently - Managing resources and costs 1. Tune query and pipeline performance 2. Control operational costs 3. Optimize storage and compute usage
Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Implement fault tolerance and recovery 2. Manage logging and alerting 3. Monitor data pipelines and workloads - Building data processing systems 1. Implement streaming data systems 2. Implement batch processing systems 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services

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Google Data Engineer GCP-DE Prüfungsfragen mit Lösungen (Q10-Q15):

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

Antwort: A

11. Frage
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Dialogflow Enterprise Edition
B. Cloud Speech-to-Text API
C. Cloud AutoML Natural Language
D. Cloud Natural Language API

Antwort: C

12. Frage
Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A. Use K-means Clustering to detect faces in the pixels.
B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Antwort: C

Begründung:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

13. Frage
As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
B. Introduce resource hierarchy to leverage access control policy inheritance.
C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
D. Use Cloud Deployment Manager to automate access provision.
E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
F. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Antwort: B,E

14. Frage
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A. Cloud Datastore
B. Cloud Bigtable
C. BigQuery
D. Cloud SQL

Antwort: D

15. Frage
......

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