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Google GCP-DE Exam Syllabus Topics:

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

Google Data Engineer Sample Questions:

1. You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
B) Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
C) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
D) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

2. 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.)

A) Assemble 1,000 files into a tape archive (TAR) fil
B) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
C) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
D) Introduce data compression for each file to increase the rate file of file transfer.
E) Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
F) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

3. You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A) Convert all daily log tables into date-partitioned tables
B) Create separate views to cover each month, and query from these views
C) Convert the sharded tables into a single partitioned table
D) Enable query caching so you can cache data from previous months

4. You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?

A) Use Cloud SQL for storag
B) Use Cloud Dataflow to transform data to support query patterns.
C) Add secondary indexes to support query patterns.
D) Use Cloud Dataflow to transform data to support query patterns.
E) Use Cloud Spanner for storag
F) Use Cloud SQL for storag
G) Add secondary indexes to support query patterns.
H) Use Cloud Spanner for storag

5. You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A) Cloud Datastore
B) BigQuery
C) Cloud SQL for PostgreSQL
D) Cloud Bigtable

Solutions:

Question # 1
Answer: A

Question # 2
Answer: C,F

Question # 3
Answer: A

Question # 4
Answer: B

Question # 5
Answer: B