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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 reliability and fault tolerance
  • 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
Building and operationalizing data processing systems25%- Deploying and managing systems
  • 1. Monitoring and logging data processes
  • 2. Managing infrastructure and resources
- Building data pipelines
  • 1. Transforming and cleaning data
  • 2. Ingesting data from various sources
  • 3. Orchestrating data workflows
Ensuring solution quality and reliability17%- 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
Maintaining and automating data workloads18%- 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
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

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Google Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
Which of these is NOT a way to customize the software on Dataproc cluster instances?

Answer: D

Explanation:
You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.
You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster. When creating a Cloud Dataproc cluster, you can specify initialization actions in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/docs/ concepts/configuring-clusters/init-actions] Reference:
https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster-properties


NEW QUESTION # 45
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

Answer: D


NEW QUESTION # 46
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:
Explanation/Reference: https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understand- your-mobile-app?hl=am


NEW QUESTION # 47
A web server sends click events to a Pub/Sub topic as messages. The web server includes an event Timestamp attribute in the messages, which is the time when the click occurred. You have a Dataflow streaming job that reads from this Pub/Sub topic through a subscription, applies some transformations, and writes the result to another Pub/Sub topic for use by the advertising department. The advertising department needs to receive each message within 30 seconds of the corresponding click occurrence, but they report receiving the messages late. Your Dataflow job's system lag is about 5 seconds, and the data freshness is about 40 seconds. Inspecting a few messages show no more than 1 second lag between their event Timestamp and publish Time. What is the problem and what should you do?

Answer: C

Explanation:
To ensure that the advertising department receives messages within 30 seconds of the click occurrence, and given the current system lag and data freshness metrics, the issue likely lies in the processing capacity of the Dataflow job. Here's why option B is the best choice:
* System Lag and Data Freshness:
* The system lag of 5 seconds indicates that Dataflow itself is processing messages relatively quickly.
* However, the data freshness of 40 seconds suggests a significant delay before processing begins, indicating a backlog.
* Backlog in Pub/Sub Subscription:
* A backlog occurs when the rate of incoming messages exceeds the rate at which the Dataflow job can process them, causing delays.
* Optimizing the Dataflow Job:
* To handle the incoming message rate, the Dataflow job needs to be optimized or scaled up by increasing the number of workers, ensuring it can keep up with the message inflow.
Steps to Implement:
* Analyze the Dataflow Job:
* Inspect the Dataflow job metrics to identify bottlenecks and inefficiencies.
* Optimize Processing Logic:
* Optimize the transformations and operations within the Dataflow pipeline to improve processing efficiency.
* Increase Number of Workers:
* Scale the Dataflow job by increasing the number of workers to handle the higher load, reducing the backlog.
Reference Links:
* Dataflow Monitoring
* Scaling Dataflow Jobs


NEW QUESTION # 48
Your company is in a highly regulated industry. One of your requirements is to ensure individual users
have access only to the minimum amount of information required to do their jobs. You want to enforce this
requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

Answer: A,C,D


NEW QUESTION # 49
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