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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Designing data processing systems (~30% of the exam)30%- Designing data pipelines
  • 1. Processing logic
  • 2. Data acquisition and import
  • 3. Streaming (e.g., windowing, late arriving data)
  • 4. Integrating with new data sources
  • 5. AI data enrichment
  • 6. Batch processing
- Selecting appropriate storage technologies
  • 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB
  • 2. Mapping storage options to business requirements
- Designing data processing resources
  • 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run)
  • 2. Cluster sizing and autoscaling
  • 3. Cost optimization
Topic 2: Preparing and using data for analysis (~15% of the exam)15%- Sharing data securely
  • 1. Data sharing and collaboration
  • 2. Publishing datasets
- Preparing data for visualization
  • 1. Preparing data for reporting and dashboards
  • 2. Connecting to Looker and other BI tools
Topic 3: Ingesting and processing the data (~20% of the exam)20%- Deploying and operationalizing the pipelines
  • 1. Job automation and orchestration (Cloud Composer, Workflows)
  • 2. CI/CD for data pipelines
- Building and maintaining data structures and databases
  • 1. Planning for analytical and operational use cases
  • 2. Defining data lifecycle
- Performing security considerations
  • 1. Data encryption
  • 2. Auditing, privacy, and compliance
  • 3. Identity and Access Management (IAM)
Topic 4: Maintaining and automating data workloads (~15% of the exam)15%- Designing for reliability and fidelity
  • 1. Recovering from failures
  • 2. Performing data quality and validation checks
  • 3. Planning for monitoring and alerting
- Monitoring data pipelines and data processes
  • 1. Managing quotas and resource usage
  • 2. Logging, monitoring, and troubleshooting
- Automating data processes
  • 1. Continuous integration and continuous deployment (CI/CD)
  • 2. Workflow orchestration
  • 3. Scheduling jobs
Topic 5: Storing the data (~20% of the exam)20%- Selecting storage systems
  • 1. Planning for storage costs and performance
  • 2. Analyzing data access patterns
  • 3. Lifecycle management of data
- Planning for using a data warehouse
  • 1. Designing the data model
  • 2. Deciding the degree of data normalization
  • 3. Mapping business requirements
  • 4. Defining architecture to support data access patterns
- Using a data lake
  • 1. Processing data
  • 2. Managing the lake (data discovery, access, cost controls)
  • 3. Monitoring the data lake
- Designing for a data platform
  • 1. Building a federated governance model for distributed data systems
  • 2. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage

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Google Certified Professional Data Engineer Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
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: A

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. References:
* 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 # 137
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

Answer: A,B


NEW QUESTION # 138
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process.
What should you do?

Answer: C

Explanation:
https://cloud.google.com/dataproc/docs/tutorials/workflow-composer
1) Create a Dataproc workflow template that runs a Spark PI job
2) Create an Apache Airflow DAG that Cloud Composer will use to start the workflow at a specific time.


NEW QUESTION # 139
You are building a model to make clothing recommendations. You know a user's fashion pis likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Answer: C

Explanation:
We have to use a combination of old and new test data as well as training data.


NEW QUESTION # 140
Which of the following is NOT true about Dataflow pipelines?

Answer: C

Explanation:
Explanation
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/


NEW QUESTION # 141
......

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