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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
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. Data sharing and collaboration 2. Publishing datasets
Ingesting and processing the data (~20% of the exam)	20%	- Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases - Performing security considerations 1. Auditing, privacy, and compliance 2. Identity and Access Management (IAM) 3. Data encryption - Deploying and operationalizing the pipelines 1. Job automation and orchestration (Cloud Composer, Workflows) 2. CI/CD for data pipelines
Storing the data (~20% of the exam)	20%	- 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 - Selecting storage systems 1. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance - 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. Deciding the degree of data normalization 3. Mapping business requirements 4. Defining architecture to support data access patterns
Maintaining and automating data workloads (~15% of the exam)	15%	- Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage - Automating data processes 1. Continuous integration and continuous deployment (CI/CD) 2. Workflow orchestration 3. Scheduling jobs - Designing for reliability and fidelity 1. Recovering from failures 2. Performing data quality and validation checks 3. Planning for monitoring and alerting
Designing data processing systems (~30% of the exam)	30%	- Designing data processing resources 1. Cost optimization 2. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 3. Cluster sizing and autoscaling - 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 pipelines 1. Batch processing 2. Data acquisition and import 3. Processing logic 4. Streaming (e.g., windowing, late arriving data) 5. AI data enrichment 6. Integrating with new data sources

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Google Certified Professional Data Engineer Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
You migrated a data backend for an application that serves 10 PB of historical product data for analytics.
Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

A. 1. Store the products as a collection in Firestore with each product having a set of historical changes.
2. Use simple and compound queries for analytics.
3. Serve the last state data directly from Firestore to the API.
B. 1. Store the historical data in BigQuery for analytics.
2. Use a materialized view to precompute the last state of a product.
3. Serve the last state data directly from BigQuery to the API.
C. 1. Store the historical data in Cloud SQL for analytics.
2. In a separate table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.
D. 1. Store the historical data in BigQuery for analytics.
2. In a Cloud SQL table, store the last state of the product after every product change.
3. Serve the last state data directly from Cloud SQL to the API.

Answer: B

NEW QUESTION # 74
You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

A. Increase the cluster size with more non-preemptible workers.
B. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
C. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
D. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Answer: B

Explanation:
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

NEW QUESTION # 75
Your financial services company has a critical daily reconciliation process that involves several distinct steps:
fetching data from an external SFTP server, decrypting the files, loading them into Cloud Storage, and finally running a series of BigQuery SQL transformations. Each step has strict dependencies, and the entire process should notify you if not completed by 7:00 AM. Manual intervention for failures is costly and delays compliance reporting. You need a highly observable and robust solution that supports easy re-runs of individual steps if errors occur. What should you do?

A. Implement a Cloud Composer DAG, with each step defined as a separate task using appropriate Airflow operators, and schedule the DAG for daily execution.
B. Create a Cloud Composer DAG that includes a single BashOperator to execute a top-level shell script, which in turn calls individual scripts for each pipeline step. Upload the scripts to a Cloud Composer environment's DAGs folder, and configure it to run daily.
C. Develop a Cloud Composer DAG that includes a single PythonOperator to execute a Python script that runs each step sequentially, incorporating error handling and retries. Upload the scripts to a Cloud Composer environment's DAGs folder, and configure it to run daily.
D. Define a Cloud Composer DAG to orchestrate the SFTP fetch and decryption steps, and then use Cloud Scheduler to trigger a separate Dataflow job that handles the Cloud Storage load and BigQuery transformations and schedule to run daily.

Answer: A

Explanation:
Cloud Composer (based on Apache Airflow) is designed for workflow orchestration where complex dependencies exist. To achieve high observability and robustness (specifically the ability to re-run individual steps), the workflow must be decomposed into granular tasks.
* Granularity and Re-runs: By defining each step (SFTP fetch, Decrypt, GCS Load, BQ Transform) as a separate task in a DAG, Airflow tracks the state of each individually. If the "BigQuery SQL" step fails, you can "Clear" only that specific task in the UI to re-run it without re-fetching or re-decrypting the data, saving time and costs.
* Observability: Each task has its own logs and status in the Airflow UI. Options B and C (Single Operator/Script) are "black boxes"-if they fail, Airflow only knows the entire script failed, making it difficult to pinpoint where and impossible to re-run just the failed portion.
* SLA and Notifications: Airflow has built-in sla_miss_callbacks and email_on_failure features. You can set an sla parameter of 7:00 AM to automatically trigger alerts if the process is lagging.
* Correcting other options: * A: Splitting the logic between Composer and a separate Scheduler
/Dataflow job breaks the end-to-end lineage and makes it harder to manage dependencies and global SLAs.
* B & C: As mentioned, using a single operator for multiple logic steps defeats the purpose of an orchestrator's monitoring and retry capabilities.
Reference: Google Cloud Documentation on Cloud Composer / Airflow:
"An Airflow DAG is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies... Breaking down your workflow into multiple tasks allows for: Individual retries (re-running only failed parts), Parallel execution, and Clearer monitoring in the Airflow web interface." (Source: Key Airflow Concepts)
"You can use the SLA (Service Level Agreement) feature in Airflow to track whether a task or DAG takes longer than expected to finish... If a task exceeds its SLA, Airflow can send an email alert or trigger a callback function." (Source: Airflow Documentation - SLAs)

NEW QUESTION # 76
You are building an application to share financial market data with consumers, who will receive data feeds.
Data is collected from the markets in real time. Consumers will receive the data in the following ways:
* Real-time event stream
* ANSI SQL access to real-time stream and historical data
* Batch historical exports
Which solution should you use?

A. Cloud Pub/Sub, Cloud Storage, BigQuery
B. Cloud Dataflow, Cloud SQL, Cloud Spanner
C. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL
D. Cloud Dataproc, Cloud Dataflow, BigQuery

Answer: B

NEW QUESTION # 77
Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

A. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
B. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.
C. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.
D. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.

Answer: B

Explanation:
For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.
Reference:
Connected Sheets | Google Sheets | Google for Developers
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Engineer Data in Google Cloud | Google Cloud Skills Boost - Qwiklabs

NEW QUESTION # 78
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