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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
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.)

A. Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
B. Configure your Cloud Dataflow pipeline to use local execution
C. Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
D. Increase the number of nodes in the Cloud Bigtable cluster
E. Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

Answer: A,E

NEW QUESTION # 90
You are architecting a data transformation solution for BigQuery. Your developers are proficient with SOL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?

A. Use Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.
B. Use Data Fusion to build and execute ETL pipelines
C. Use Dataform to build, manage, and schedule SQL pipelines.
D. Use Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.

Answer: C

Explanation:
To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here' s why:
* ELT Development Technique:
* ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.
* BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.
* Dataform:
* Dataformis a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.
* It provides tools for managing SQL as code, including version control and collaborative development.
* Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.
* Intuitive Coding Environment:
* Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.
* It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.
* Managing SQL as Code:
* Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.
* This allows for better collaboration, code reviews, and version tracking.
Reference Links:
* Dataform Documentation
* BigQuery Documentation
* Managing ELT Pipelines with Dataform

NEW QUESTION # 91
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
B. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
D. Create a cron schedule in Cloud Dataprep.

Answer: B

NEW QUESTION # 92
Why do you need to split a machine learning dataset into training data and test data?

A. So you can use one dataset for a wide model and one for a deep model
B. So you can try two different sets of features
C. To make sure your model is generalized for more than just the training data
D. To allow you to create unit tests in your code

Answer: C

Explanation:
The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.

NEW QUESTION # 93
A live TV show asks viewers to cast votes using their mobile phones. The event generates a large volume of data during a 3 minute period. You are in charge of the Voting restructure* and must ensure that the platform can handle the load and Hal all votes are processed. You must display partial results write voting is open.
After voting doses you need to count the votes exactly once white optimizing cost. What should you do?

A. Write votes to a Pub Sub tope and have Cloud Functions subscribe to it and write voles to BigQuery
B. Write votes to a Pub/Sub tope and toad into both Bigtable and BigQuery via a Dataflow pipeline Query Bigtable for real-time results and BigQuery for later analysis Shutdown the Bigtable instance when voting concludes D Create a Cloud SQL for PostgreSQL database with high availability (HA) configuration and multiple read replicas
C. Create a Memorystore instance with a high availability (HA) configuration

Answer: B

NEW QUESTION # 94
......

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