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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
- 8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
- 3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
- 60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
* Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
* 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
Connect a VPN between the production data center and cloud environment
SEO Statement
We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Dataflow, Cloud SQL, and Cloud Storage
B. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
E. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

Answer: C

Explanation:
Explanation

NEW QUESTION # 84
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
B. Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
C. Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
D. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Answer: A

Explanation:
highly available = multi-regional:
https://cloud.google.com/bigquery/docs/locations
recovery strategy of this data that minimizes cost = point-in-time snapshot:
https://cloud.google.com/solutions/bigquery-data-warehouse#backup-and-recovery

NEW QUESTION # 85
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

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

Answer: C

Explanation:
Topic 5, Practice Questions

NEW QUESTION # 86
Your company's customer and order databases are often under heavy load. This makes performing
analytics against them difficult without harming operations. The databases are in a MySQL cluster, with
nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations.
What should you do?

A. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
B. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
C. Add a node to the MySQL cluster and build an OLAP cube there.
D. Use an ETL tool to load the data from MySQL into Google BigQuery.

Answer: B

NEW QUESTION # 87
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

A. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
B. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
C. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
D. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Answer: A

Explanation:
https://cloud.google.com/compute/docs/disks/customer-managed-encryption

NEW QUESTION # 88
......

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