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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q96-Q101):

NEW QUESTION # 96
When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

Answer: C

Explanation:
Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It is not a good solution for less than 1 TB of data.
Reference: https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options


NEW QUESTION # 97
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Answer: D

Explanation:
Explanation


NEW QUESTION # 98
You orchestrate ETL pipelines by using Cloud Composer. One of the tasks in the Apache Airflow directed acyclic graph (DAG) relies on a third-party service. You want to be notified when the task does not succeed. What should you do?

Answer: D


NEW QUESTION # 99
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

Answer: C

Explanation:
Explanation


NEW QUESTION # 100
Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

Answer: B

Explanation:
By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries.
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-
series#ensure_that_your_row_key_avoids_hotspotting


NEW QUESTION # 101
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