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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Preparing data for analysis and machine learning	13%	- Enabling data analysis 1. Implementing data sharing and governance 2. Preparing datasets for querying and reporting 3. Optimizing query performance - Preparing data for ML 1. Feature engineering and data preparation 2. Supporting model training and serving
Designing data processing systems	24%	- Designing data pipelines 1. Defining architecture for batch and streaming processing 2. Planning for data migration and integration 3. Designing for data transformation and enrichment - Planning data solutions 1. Planning for data security and compliance 2. Selecting appropriate storage solutions 3. Designing for reliability, scalability, and efficiency
Ingesting and processing data	25%	- Building data pipelines 1. Developing batch and streaming workflows 2. Handling data quality and consistency 3. Implementing ingestion mechanisms - Transforming data 1. Optimizing transformations for performance 2. Applying data processing logic 3. Managing schema evolution
Maintaining and automating data workloads	18%	- Operationalizing workloads 1. Orchestrating and scheduling jobs 2. Ensuring reliability and recoverability 3. Monitoring and troubleshooting pipelines - Automation and optimization 1. Automating deployment and management 2. Optimizing resource usage and costs
Storing and managing data	20%	- Implementing storage solutions 1. Designing data warehouses and data lakes 2. Using data storage services appropriately 3. Managing data lifecycle and retention - Optimizing storage performance and cost 1. Configuring storage for access patterns 2. Implementing cost optimization strategies

Google Data Engineer Sample Questions:

Question 1

What are two methods that can be used to denormalize tables in BigQuery?

A. 1) Split table into multiple tables; 2) Use a partitioned table
B. 1) Use nested repeated fields; 2) Use a partitioned table
C. 1) Use a partitioned table; 2) Join tables into one table
D. 1) Join tables into one table; 2) Use nested repeated fields

Question 2

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

Question 3

What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and loading data from a file
B. Storage, queries, and exporting data
C. Queries and streaming inserts
D. Storage, queries, and streaming inserts

Question 4

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

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

Question 5

Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can be programmed in Java
B. Dataflow pipelines can consume data from other Google Cloud services
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

Solutions:

Question 1
Answer: D

Question 2
Answer: B

Question 3
Answer: D

Question 4
Answer: D

Question 5
Answer: C