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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing machine learning models	26%	- ML pipeline integration 1. Feature engineering and feature stores 2. Vertex AI pipeline deployment - Model deployment and monitoring 1. Model monitoring and drift detection 2. Online vs batch prediction
Topic 2: Designing data processing systems	22%	- Batch and streaming data processing design 1. Latency, throughput, and consistency trade-offs 2. Event-driven vs batch architectures - Data architecture and storage design 1. Designing scalable and cost-effective data models 2. Choosing appropriate data storage solutions (relational, NoSQL, data warehouse)
Topic 3: Ensuring solution quality	28%	- Security and governance 1. Data governance and compliance 2. IAM and access control in GCP - Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads
Topic 4: Building and operationalizing data processing systems	24%	- Data ingestion and integration 1. Streaming ingestion (Pub/Sub, Dataflow) 2. Batch ingestion pipelines (BigQuery, Cloud Storage) - Data processing and transformation 1. Using Dataproc, Dataflow, and BigQuery SQL 2. ETL/ELT pipeline design

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q245-Q250):

245. Frage
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of dat
a. They want to improve this performance while minimizing cost. What should they do?

A. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
B. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.
C. Redefine the schema by evenly distributing reads and writes across the row space of the table.
D. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

Antwort: C

246. Frage
You work for a large ecommerce company. You store your customers order data in Bigtable. You have a garbage collection policy set to delete the data after 30 days and the number of versions is set to 1. When the data analysts run a query to report total customer spending, the analysts sometimes see customer data that is older than 30 days. You need to ensure that the analysts do not see customer data older than 30 days while minimizing cost and overhead. What should you do?

A. Set the expiring values of the column families to 29 days and keep the number of versions to 1.
B. Set the expiring values of the column families to 30 days and set the number of versions to 2.
C. Schedule a job daily to scan the data in the table and delete data older than 30 days.
D. Use a timestamp range filter in the query to fetch the customer's data for a specific range.

Antwort: D

Begründung:
By using a timestamp range filter in the query, you can ensure that the analysts only see the customer data that is within the desired time range, regardless of the garbage collection policy1. This option is the most cost-effective and simple way to avoid fetching data that is marked for deletion by garbage collection, as it does not require changing the existing policy or creating additional jobs. You can use the Bigtable client libraries or the cbt CLI to apply a timestamp range filter to your read requests2.
Option A is not effective, as it increases the number of versions to 2, which may cause more data to be retained and increase the storage costs. Option C is not reliable, as it reduces the expiring values to 29 days, which may not match the actual data arrival and usage patterns. Option D is not efficient, as it requires scheduling a job daily to scan and delete the data, which may incur additional overhead and complexity. Moreover, none of these options guarantee that the data older than 30 days will be immediately deleted, as garbage collection is an asynchronous process that can take up to a week to remove the data3. Reference:
1: Filters | Cloud Bigtable Documentation | Google Cloud
2: Read data | Cloud Bigtable Documentation | Google Cloud
3: Garbage collection overview | Cloud Bigtable Documentation | Google Cloud

247. Frage
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery. Your access pattern is based on recent data tittered by location_id and device_version with the following query:

You want to optimize your queries for cost and performance. How should you structure your data?

A. Cluster table data by create_date location_id and device_version
B. Cluster table data by create_date, partition by location and device_version
C. Partition table data by create_date, location_id and device_version
D. Partition table data by create_date cluster table data by tocation_id and device_version

Antwort: D

248. Frage
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. 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.
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 environments DAGs folder, and configure it to run daily.
D. Implement a Cloud Composer DAG, with each step defined as a separate task using appropriate Airflow operators, and schedule the DAG for daily execution.

Antwort: D

Begründung:
Defining each step as a separate task in a Cloud Composer DAG provides fine-grained dependency management, built-in observability, and clear task-level monitoring. This approach allows individual steps to be retried or re-run independently, supports robust failure handling and alerting for SLA breaches such as missing the 7:00 AM deadline, and aligns with best practices for orchestrating complex, dependent data pipelines.

249. Frage
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Antwort: B

250. Frage
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