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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Ensuring data privacy and compliance 2. Implementing access control and data protection
Topic 2: Building and operationalizing data processing systems	25%	- Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes - Building data pipelines 1. Ingesting data from various sources 2. Transforming and cleaning data 3. Orchestrating data workflows
Topic 3: Ensuring solution quality and reliability	17%	- Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues - Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing
Topic 4: Maintaining and automating data workloads	18%	- Resource optimization 1. Choosing appropriate compute and storage options 2. Cost management and resource allocation - Automation and repeatability 1. Automating deployment and updates 2. Implementing CI/CD for data systems
Topic 5: 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. Handling structured and unstructured data 2. Feature engineering and data preparation

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Google Certified Professional Data Engineer Exam Sample Questions (Q218-Q223):

NEW QUESTION # 218
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error. SELECT person FROM
`project1.example.table1` WHERE city = "London" How would you correct the error?

A. Add ", UNNEST(person)" before the WHERE clause.
B. Add ", UNNEST(city)" before the WHERE clause.
C. Change "person" to "city.person".
D. Change "person" to "person.city".

Answer: A

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy- sql#nested_repeated_results

NEW QUESTION # 219
You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub
/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

A. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore
B. Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.
C. Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore
D. Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.

Answer: A

Explanation:
A hopping window is a type of sliding window that advances by a fixed period of time, producing overlapping windows. This is suitable for the scenario where the system needs to aggregate data for the last
30 seconds, every 2 seconds, and provide real-time updates. A Dataflow pipeline can implement the hopping window logic using Apache Beam, and process both streaming and batch data sources. Memorystore is a low- latency, in-memory data store that can serve the aggregated data to the visualization layer. BigQuery is not a good choice for this scenario, as it is not optimized for low-latency queries and frequent updates.

NEW QUESTION # 220
You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

A. 1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
B. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone.3. Assign permissions to the data engineering teams at the zone level.
C. 1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
D. 1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data.
2 Provide each data engineering team access to the virtual lake.

Answer: C

Explanation:
This option is the best way to configure Dataplex for a data mesh architecture, as it allows each data engineering team to have full ownership and control over their data products, while also enabling easy discovery and sharing of the curated data across the organization12. By creating a Dataplex virtual lake for each data product,you can isolate the data assets and resources for each domain, and avoid conflicts and dependencies between different teams3. By creating multiple zones for landing, raw, and curated data, you can enforce different security and governance policies for each stage of the data curation process, and ensure that only authorized users can access the data assets45. By providing the data engineering teams with full access to the virtual lake assigned to their data product, you can empower them to manage and monitor their data products, and leverage the Dataplex features such as tagging, quality, and lineage.
Option A is not suitable, as it creates a single point of failure and a bottleneck for the data mesh, and does not allow for fine-grained access control and governance for different data products2. Option B is also not suitable, as it does not isolate the data assets and resources for each data product, and assigns permissions at the zone level, which may not reflect the different roles and responsibilities of the data engineering teams34. Option C is better than option A and B, but it does not create multiple zones forlanding, raw, and curated data, which may compromise the security and quality of the data products5. References:
1: Building a data mesh on Google Cloud using BigQuery and Dataplex | Google Cloud Blog
2: Data Mesh - 7 Effective Practices to Get Started - Confluent
3: Best practices | Dataplex | Google Cloud
4: Secure your lake | Dataplex | Google Cloud
5: Zones | Dataplex | Google Cloud
[6]: Managing a Data Mesh with Dataplex - ROI Training

NEW QUESTION # 221
Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

A. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
B. Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.
C. Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.
D. Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.

Answer: C

Explanation:
From Cloud SQL we can fetch the record on timestamp basis using where clause and it satisfies near real time.

NEW QUESTION # 222
You have enabled the free integration between Firebase Analytics and Google BigQuery.
Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL.
What should you do?

A. Use the TABLE_DATE_RANGE function
B. Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
C. Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D. Use the WHERE_PARTITIONTIME pseudo column

Answer: A

Explanation:
Legacy sql uses table date range whereas standard sql uses table_sufix for wildcard.

NEW QUESTION # 223
......

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