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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Designing data processing systems20%- 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
Building and operationalizing data processing systems25%- 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. Orchestrating data workflows
  • 3. Transforming and cleaning data
Operationalizing machine learning models20%- 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
Maintaining and automating data workloads18%- Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Performance and scalability testing
  • 2. Data quality validation
- Troubleshooting and optimization
  • 1. Diagnosing performance issues
  • 2. Optimizing queries and workloads

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Google Certified Professional Data Engineer Exam Sample Questions (Q388-Q393):

NEW QUESTION # 388
What are all of the BigQuery operations that Google charges for?

Answer: D

Explanation:
Explanation
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing


NEW QUESTION # 389
Which software libraries are supported by Cloud Machine Learning Engine?

Answer: D

Explanation:
Cloud ML Engine mainly does two things:
Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.
Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.
Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does


NEW QUESTION # 390
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?

Answer: C

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-app


NEW QUESTION # 391
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

Answer: B

Explanation:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case of zonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the
-zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. References:
* 1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
* 3: Regional endpoints | Cloud Dataflow | Google Cloud


NEW QUESTION # 392
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?

Answer: D

Explanation:
Explanation/Reference:
Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-
understand-your-mobile-app?hl=am


NEW QUESTION # 393
......

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