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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and automating data workloads	18%	- Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems - Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation
Operationalizing machine learning models	20%	- Preparing data for ML 1. Handling structured and unstructured data 2. Feature engineering and data preparation - Deploying and maintaining ML models 1. Optimizing model performance and cost 2. Model serving and monitoring
Designing data processing systems	20%	- Designing for business requirements 1. Designing for reliability and fault tolerance 2. Selecting appropriate storage solutions 3. Designing for scalability and elasticity - Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance
Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Diagnosing performance issues 2. Optimizing queries and workloads
Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Monitoring and logging data processes 2. Managing infrastructure and resources - Building data pipelines 1. Ingesting data from various sources 2. Orchestrating data workflows 3. Transforming and cleaning data

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Google Certified Professional Data Engineer Exam Sample Questions (Q332-Q337):

NEW QUESTION # 332
Your platform on your on-premises environment generates 100 GB of data daily, composed of millions of structured JSON text files. Your on-premises environment cannot be accessed from the public internet. You want to use Google Cloud products to query and explore the platform data. What should you do?

A. Use a Transfer Appliance to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
B. Use the BigQuery Data Transfer Service dataset copy to transfer all data into BigQuery.
C. Use Transfer Service for on-premises data to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
D. Use Cloud Scheduler to copy data daily from your on-premises environment to Cloud Storage.
Use the BigQuery Data Transfer Service to import data into BigQuery.

Answer: C

Explanation:
Your on-premises environment cannot be accessed from the public internet.
It signifies that we can apply private connection like Cloud Interconnect.
Reference:
https://cloud.google.com/network-connectivity/docs/interconnect/concepts/overview

NEW QUESTION # 333
Case Study 2 - MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production - to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco is building a custom interface to share data. They have these requirements:
They need to do aggregations over their petabyte-scale datasets. They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

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

Answer: B

NEW QUESTION # 334
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
B. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
C. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
D. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

Answer: A

NEW QUESTION # 335
You want to store your team's shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

A. Give analysts the BigQuery Data Viewer role at the project level. Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.
B. Give analysts the BigQuery Data Viewer role on the shared dataset. Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.
C. Give analysts the BigQuery Data Viewer role at the project level. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.
D. Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset.

Answer: D

NEW QUESTION # 336
Which of these is not a supported method of putting data into a partitioned table?

A. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
B. Create a partitioned table and stream new records to it every day.
C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Answer: C

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.

NEW QUESTION # 337
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