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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
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
Ensuring solution quality and reliability	17%	- Troubleshooting and optimization 1. Diagnosing performance issues 2. Optimizing queries and workloads - Testing and validating data systems 1. Performance and scalability testing 2. Data quality validation
Designing data processing systems	20%	- Designing for regulatory and security requirements 1. Ensuring data privacy and compliance 2. Implementing access control and data protection - Designing for business requirements 1. Designing for reliability and fault tolerance 2. Designing for scalability and elasticity 3. Selecting appropriate storage solutions
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
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. Monitoring and logging data processes 2. Managing infrastructure and resources

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Google Certified Professional Data Engineer Exam Sample Questions (Q246-Q251):

NEW QUESTION # 246
Which methods can be used to reduce the number of rows processed by BigQuery?

A. Splitting tables into multiple tables; using the LIMIT clause
B. Putting data in partitions; using the LIMIT clause
C. Splitting tables into multiple tables; putting data in partitions
D. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause

Answer: C

Explanation:
Explanation
If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day.
If you use the LIMIT clause, BigQuery will still process the entire table.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

NEW QUESTION # 247
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl.
being able to reprocess all failing data).
What should you do?

A. Add a try... catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.
B. Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.
C. Add a try... catch block to your DoFn that transforms the data, extract erroneous rows from logs.
D. Add a try... catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

Answer: A

NEW QUESTION # 248
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

A. Windows at every 100 MB of data
B. Single, Global Window
C. Windows at every 10 minutes
D. Windows at every 1 minute

Answer: B

Explanation:
Explanation
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION # 249
You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:
- No interaction by the user on the site for 1 hour
- Has added more than $30 worth of products to the basket
- Has not completed a transaction
You use Google Cloud Dataflow to process the data and decide if a message should be sent.
How should you design the pipeline?

A. Use a sliding time window with a duration of 60 minutes.
B. Use a global window with a time based trigger with a delay of 60 minutes.
C. Use a fixed-time window with a duration of 60 minutes.
D. Use a session window with a gap time duration of 60 minutes.

Answer: D

Explanation:
It will send a message per user after that user is inactive for 60 minutes. Session window works well for capturing a session per user basis.

NEW QUESTION # 250
Which of the following statements is NOT true regarding Bigtable access roles?

A. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
B. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
C. You can configure access control only at the project level.
D. To give a user access to only one table in a project, you must configure access through your application.

Answer: B

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project. Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances.
Reference:
https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 251
......

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