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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Maintaining and automating data workloads	18%	- Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems - Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options
Topic 2: 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
Topic 3: Building and operationalizing data processing systems	25%	- Building data pipelines 1. Transforming and cleaning data 2. Ingesting data from various sources 3. Orchestrating data workflows - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes
Topic 4: Operationalizing machine learning models	20%	- Preparing data for ML 1. Feature engineering and data preparation 2. Handling structured and unstructured data - Deploying and maintaining ML models 1. Model serving and monitoring 2. Optimizing model performance and cost
Topic 5: Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Implementing access control and data protection 2. Ensuring data privacy and compliance - Designing for business requirements 1. Selecting appropriate storage solutions 2. Designing for scalability and elasticity 3. Designing for reliability and fault tolerance

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Google Certified Professional Data Engineer Exam Sample Questions (Q339-Q344):

NEW QUESTION # 339
Case Study 1 - Flowlogistic
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 CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A. Create a view on the table to present to the virtualization tool.
B. Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.
C. Export the data into a Google Sheet for virtualization.
D. Create an additional table with only the necessary columns.

Answer: A

NEW QUESTION # 340
You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

A. Store the warm data as files in Cloud Storage, and store the active data in BigQuery. Keep this ratio as
80% warm and 20% active.
B. Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
C. Store and process the entire dataset in Cloud Bigtable.
D. Store and process the entire dataset in BigQuery.

Answer: A

NEW QUESTION # 341
You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries.
What should you do?

A. Create a sales_transaction table that holds the sales_transaction_header information as rows and thesales_transaction_line rows as nested and repeated fields.
B. Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.
C. Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.
D. Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.

Answer: A

Explanation:
BigQuery supports nested and repeated fields, which are complex data types that can represent hierarchical and one-to-many relationships within a single table. By using nested and repeated fields, you can denormalize your data model and reduce the number of joins required for your queries. This can improve the performance and efficiency of your data analytics queries, as joins can be expensive and require shuffling data across nodes. Nested and repeated fields also preserve the data integrity and avoid data duplication. In this scenario, the sales_transaction_header and sales_transaction_line tables have a tightly coupled immutable relationship, meaning that each header row corresponds to one or more line rows, and the data is rarely modified after load.
Therefore, it makes sense to create a single sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields. This way, you can query the sales transaction data without joining two tables, and use dot notation or array functions to access the nested and repeated fields. For example, the sales_transaction table could have the following schema:
Table
Field name
Type
Mode
id
INTEGER
NULLABLE
order_time
TIMESTAMP
NULLABLE
customer_id
INTEGER
NULLABLE
line_items
RECORD
REPEATED
line_items.sku
STRING
NULLABLE
line_items.quantity
INTEGER
NULLABLE
line_items.price
FLOAT
NULLABLE
To query the total amount of each order, you could use the following SQL statement:
SQL
SELECTid,SUM(line_items.quantity*line_items.price)AStotal_amount
FROMsales_transaction
GROUPBYid;
AI-generated code. Review and use carefully. More info on FAQ.
References:
Use nested and repeated fields
BigQuery explained: Working with joins, nested & repeated data
Arrays in BigQuery - How to improve query performance and optimise storage

NEW QUESTION # 342
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions.
You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?

A. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
B. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
C. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
D. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION # 343
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour.
How should you design the solution?

A. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
B. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
C. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
D. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.

Answer: C

NEW QUESTION # 344
......

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