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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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. Optimizing model performance and cost
  • 2. Model serving and monitoring
Topic 2: Building and operationalizing data processing systems25%- Building data pipelines
  • 1. Ingesting data from various sources
  • 2. Transforming and cleaning data
  • 3. Orchestrating data workflows
- Deploying and managing systems
  • 1. Monitoring and logging data processes
  • 2. Managing infrastructure and resources
Topic 3: Maintaining and automating data workloads18%- Automation and repeatability
  • 1. Implementing CI/CD for data systems
  • 2. Automating deployment and updates
- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
Topic 4: Ensuring solution quality and reliability17%- 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 5: Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Designing for scalability and elasticity
  • 2. Selecting appropriate storage solutions
  • 3. Designing for reliability and fault tolerance

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Google Certified Professional Data Engineer Exam Sample Questions (Q105-Q110):

NEW QUESTION # 105
Cloud Bigtable is Google's ______ Big Data database service.

Answer: D

Explanation:
Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail. It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.
Reference:
https://cloud.google.com/bigtable/


NEW QUESTION # 106
Scaling a Cloud Dataproc cluster typically involves ____.

Answer: D

Explanation:
After creating a Cloud Dataproc cluster, you can scale the cluster by increasing or decreasing the number of worker nodes in the cluster at any time, even when jobs are running on the cluster. Cloud Dataproc clusters are typically scaled to:
1) increase the number of workers to make a job run faster
2) decrease the number of workers to save money
3) increase the number of nodes to expand available Hadoop Distributed Filesystem (HDFS) storage


NEW QUESTION # 107
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

Answer: D

Explanation:
Fusion is an optimization technique that Dataflow applies to merge multiple transforms into a single stage. This reduces the overhead of shuffling data between stages, but it can also limit the parallelism and scalability of the pipeline. By introducing a Reshuffle step, you can force Dataflow to split the pipeline into multiple stages, which can increase the number of workers that can process the data in parallel. Reshuffle also adds randomness to the data distribution, which can help balance the workload across workers and avoid hot keys or skewed data. Reference:
1: Streaming pipelines
2: Batch vs Streaming Performance in Google Cloud Dataflow
3: Deploy Dataflow pipelines
4: How Distributed Shuffle improves scalability and performance in Cloud Dataflow pipelines
5: Managing costs for Dataflow batch and streaming data processing


NEW QUESTION # 108
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

Answer: A


NEW QUESTION # 109
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

Answer: A

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time ?the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers


NEW QUESTION # 110
......

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