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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q211-Q216):

211. Frage
A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard -32) takes two days to complete framing. The model has custom TensorFlow operations that must run partially on a CPU You want to reduce the training time in a cost-effective manner. What should you do?

A. Change the VM type to n2-highmem-32
B. Change the VM type to e2 standard-32
C. Train the model using a VM with a GPU hardware accelerator
D. Train the model using a VM with a TPU hardware accelerator

Antwort: C

212. Frage
You use a dataset in BigQuery for analysis. You want to provide third-party companies with access to the same dataset. You need to keep the costs of data sharing low and ensure that the data is current. Which solution should you choose?

A. Create a Cloud Dataflow job that reads the data in frequent time intervals, and writes it to the relevant BigQuery dataset or Cloud Storage bucket for third-party companies to use.
B. Create a separate dataset in BigQuery that contains the relevant data to share, and provide third-party companies with access to the new dataset.
C. Create an authorized view on the BigQuery table to control data access, and provide third-party companies with access to that view.
D. Use Cloud Scheduler to export the data on a regular basis to Cloud Storage, and provide third-party companies with access to the bucket.

Antwort: C

Begründung:
By creating an authorized view one assures that the data is current and avoids taking more storage space (and cost) in order to share a dataset. B and D are not cost optimal and C does not guarantee that the data is kept updated.

213. Frage
You are building a system to process financial transactions. The system must handle a high throughput of concurrent user operations and each operation requires low-latency reads and writes to individual records. You need to identify a storage solution that guarantees ACID compliance for the processed transactions. You want a Google Cloud managed service. What should you do?

A. Select Spanner.
B. Select BigQuery.
C. Select Cloud Storage.
D. Select Bigtable.

Antwort: A

Begründung:
Cloud Spanner is a fully managed, globally consistent database that provides ACID transactions, low-latency reads and writes, and can handle high throughput for concurrent operations, making it suitable for financial transaction processing.

214. Frage
You are analyzing the price of a company's stock. Every 5 seconds, you need to compute a moving average of the past 30 seconds' worth of data. You are reading data from Pub/Sub and using DataFlow to conduct the analysis. How should you set up your windowed pipeline?

A. Use a fixed window with a duration of 30 seconds. Emit results by setting the following trigger:
AfterWatermark.pastEndOfWindow().plusDelayOf(Duration.standardSeconds(5))
B. Use a fixed window with a duration of 5 seconds. Emit results by setting the following trigger:
AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardSeconds(30))
C. Use a sliding window with a duration of 5 seconds. Emit results by setting the following trigger:
AfterProcessingTime.pastFirstElementInPane().plusDelayOf(Duration.standardSeconds(30))
D. Use a sliding window with a duration of 30 seconds and a period of 5 seconds. Emit results by setting the following trigger: AfterWatermark.pastEndOfWindow()

Antwort: A

215. Frage
Your large e-commerce company uses BigQuery for its data warehousing needs. They have two primary types of workloads:
- Analyst dashboards: Interactive queries run by business analysts
throughout the day, requiring low latency for real-time insights.
- ETL jobs: Large, complex batch queries that load and transform data
overnight. These jobs are cost-sensitive and can tolerate longer
execution times, and they do not have the same performance requirements as the dashboards.
Analysts are reporting inconsistent performance of their dashboards, particularly during peak business hours. You need to ensure reliable performance for both workload types while optimizing costs. What should you do?

A. Implement a custom autoscaling solution using Cloud Run functions to dynamically adjust available BigQuery slots based on time of day.
B. Migrate all ETL jobs to Cloud SQL to reduce BigQuery's workload.
C. Run all analyst dashboard queries as interactive queries and configure ETL jobs to run as batch queries.
D. Use BigQuery editions to create an autoscaling reservation with dedicated slots for analyst dashboards, while running ETL jobs with a separate reservation or using the on-demand billing model.

Antwort: D

Begründung:
Using BigQuery editions with capacity reservations lets you allocate dedicated, autoscaling slots to the interactive dashboard workload so it has predictable resources and consistent latency during peak hours. Running ETL in a separate reservation or on-demand isolates the cost- sensitive batch workload from the dashboards, preventing resource contention while optimizing spend by scaling capacity appropriately for each workload type.

216. Frage
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