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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on- premises database to GCP. The database is about 20 TB in size. Which database should you choose?

Answer: D

Explanation:
Explanation/Reference:


NEW QUESTION # 20
You are creating a data model in BigQuery that will hold retail transaction dat a. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries. What should you do?

Answer: C

Explanation:
BigQuery supports nested and repeated fields, which are complex data types that can represent hierarchical and one-to-many relationships within a single table. By using nested and repeated fields, you can denormalize your data model and reduce the number of joins required for your queries. This can improve the performance and efficiency of your data analytics queries, as joins can be expensive and require shuffling data across nodes. Nested and repeated fields also preserve the data integrity and avoid data duplication. In this scenario, the sales_transaction_header and sales_transaction_line tables have a tightly coupled immutable relationship, meaning that each header row corresponds to one or more line rows, and the data is rarely modified after load. Therefore, it makes sense to create a single sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields. This way, you can query the sales transaction data without joining two tables, and use dot notation or array functions to access the nested and repeated fields. For example, the sales_transaction table could have the following schema:
Table
Field name
Type
Mode
id
INTEGER
NULLABLE
order_time
TIMESTAMP
NULLABLE
customer_id
INTEGER
NULLABLE
line_items
RECORD
REPEATED
line_items.sku
STRING
NULLABLE
line_items.quantity
INTEGER
NULLABLE
line_items.price
FLOAT
NULLABLE
To query the total amount of each order, you could use the following SQL statement:
SQL
SELECT id, SUM(line_items.quantity * line_items.price) AS total_amount
FROM sales_transaction
GROUP BY id;
AI-generated code. Review and use carefully. More info on FAQ.
Reference:
Use nested and repeated fields
BigQuery explained: Working with joins, nested & repeated data
Arrays in BigQuery - How to improve query performance and optimise storage


NEW QUESTION # 21
You are migrating your data warehouse to Google Cloud and decommissioning your on-premises data center Because this is a priority for your company, you know that bandwidth will be made available for the initial data load to the cloud. The files being transferred are not large in number, but each file is 90 GB Additionally, you want your transactional systems to continually update the warehouse on Google Cloud in real time What tools should you use to migrate the data and ensure that it continues to write to your warehouse?

Answer: C


NEW QUESTION # 22
You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

Answer: C

Explanation:
Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline.
Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline


NEW QUESTION # 23
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

Answer: B


NEW QUESTION # 24
......

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