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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
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
Maintaining and automating data workloads	18%	- Resource optimization 1. Cost management and resource allocation 2. Choosing appropriate compute and storage options - Automation and repeatability 1. Implementing CI/CD for data systems 2. Automating deployment and updates
Ensuring solution quality and reliability	17%	- Testing and validating data systems 1. Data quality validation 2. Performance and scalability testing - Troubleshooting and optimization 1. Optimizing queries and workloads 2. Diagnosing performance issues
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. Model serving and monitoring 2. Optimizing model performance and cost
Building and operationalizing data processing systems	25%	- Building data pipelines 1. Orchestrating data workflows 2. Ingesting data from various sources 3. Transforming and cleaning data - Deploying and managing systems 1. Managing infrastructure and resources 2. Monitoring and logging data processes

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Google Certified Professional Data Engineer Exam Sample Questions (Q187-Q192):

NEW QUESTION # 187
An online retailer has built their current application on Google App Engine. A new initiative at the company
mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using
a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google
Cloud database should they choose?

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

Answer: B

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/solutions/business-intelligence/

NEW QUESTION # 188
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat
a. How can you adjust your application design?

A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
B. Convert the streaming insert code to batch load for individual messages.
C. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
D. Re-write the application to load accumulated data every 2 minutes.

Answer: C

Explanation:
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue. So We need to wait till it's written tio storage

NEW QUESTION # 189
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

A. Create a table on Cloud Spanner, and insert and delete rows with the job information
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud SQL, and insert and delete rows with the job information
D. Create an API using App Engine to receive and send messages to the applications

Answer: D

NEW QUESTION # 190
You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:
SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country You check the query plan for the query and see the following output in the Read section of Stage:1:

What is the most likely cause of the delay for this query?

A. Either the state or the city columns in the [myproject:mydataset.mytable] table have too many NULL values
B. Users are running too many concurrent queries in the system
C. The [myproject:mydataset.mytable] table has too many partitions
D. Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Answer: B

NEW QUESTION # 191
Your organization uses a multi-cloud data storage strategy, storing data in Cloud Storage, and data in Amazon Web Services' (AWS) S3 storage buckets. All data resides in US regions. You want to query up-to-date data by using BigQuery. regardless of which cloud the data is stored in. You need to allow users to query the tables from BigQuery without giving direct access to the data in the storage buckets What should you do?

A. Use the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets Create external tables over the Cloud Storage data and query the data using BigQuery directly
B. Use the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets Create BigLake tables over the Cloud Storage data and query the data using BigQuery directly.
C. Set up a BigQuery Omni connection to the AWS S3 bucket data Create BigLake tables over the Cloud Storage and S3 data and query the data using BigQuery directly.
D. Set up a BigQuery Omni connection to the AWS S3 bucket data. Create external tables over the Cloud Storage and S3 data and query the data using BigQuery directly.

Answer: D

Explanation:
BigQuery Omni enables you to run BigQuery analytics directly on data stored in AWS S3 buckets without having to move or copy the data. This provides several benefits:
Reduced Data Movement Costs: Eliminates the need to egress data from AWS, potentially saving significant costs.
Real-Time Analytics: Allows you to query data in AWS S3 in real-time, providing up-to-date insights.
Simplified Architecture: Reduces the complexity of managing data pipelines and ETL processes.
Here's a breakdown of the steps involved in using BigQuery Omni:
Set up a BigQuery Omni connection: This involves configuring the connection between your Google Cloud project and your AWS S3 bucket. This connection establishes the secure link for BigQuery to access the data in AWS S3.
Create external tables: BigQuery external tables are a way to query data residing in external storage systems, such as AWS S3, without having to import the data into BigQuery. This enables you to directly query the data in its original location.
Query the data using BigQuery: Once the external tables are created, you can use standard SQL queries to analyze the data stored in both Cloud Storage and AWS S3, just as if it were native BigQuery data.
Why other options are not suitable:
Option A: BigLake tables are designed for storing large volumes of structured data within BigQuery itself, not for directly querying data in external storage systems.
Option C and D: While the Storage Transfer Service is useful for moving data between cloud providers, it introduces unnecessary data movement and latency if the goal is to simply query the data in its original location.
Key Points:
BigQuery Omni extends BigQuery's capabilities to analyze data stored in other cloud providers, such as AWS.
External tables provide a way to query data in external storage systems without having to import it into BigQuery.
By leveraging BigQuery Omni and external tables, you can efficiently and cost-effectively query data stored in multiple cloud environments using a single tool, BigQuery.

NEW QUESTION # 192
......

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