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Google Professional-Data-Engineer Certification is a popular certification for professionals in the field of data engineering. Google Certified Professional Data Engineer Exam certification validates the knowledge and skills of professionals in designing and building data processing systems, as well as their ability to analyze and use machine learning models. Google Certified Professional Data Engineer Exam certification is gaining popularity among professionals who want to validate their skills and knowledge in this field and advance their careers.

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The last section of the certification exam evaluates the ability of the learners to design for security & compliance, including identity & access management, legal compliance, data security, and privacy ensuring. Moreover, they should be able to ensure flexibility & portability, reliability & fidelity, as well as scalability & efficiency.

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Google Certified Professional Data Engineer Exam Sample Questions (Q205-Q210):

NEW QUESTION # 205
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, partition by location and device_version
B. Partition table data by create_date cluster table data by tocation_id and device_version
C. Cluster table data by create_date location_id and device_version
D. Partition table data by create_date, location_id and device_version

Answer: C

NEW QUESTION # 206
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?

A. Create an initialization action to execute the jobs
B. Create a Directed Acyclic Graph in Cloud Composer
C. Create a Cloud Dataproc Workflow Template
D. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster

Answer: C

NEW QUESTION # 207
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
B. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
C. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
D. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

Answer: C

NEW QUESTION # 208
You need to analyze user clickstream data to personalize content recommendations. The data arrives continuously and needs to be processed with low latency, including transformations such as sessionization (grouping clicks by user within a time window) and aggregation of user activity. You need to identify a scalable solution to handle millions of events each second and be resilient to late-arriving data. What should you do?

A. Use Firebase Realtime Database for ingestion and storage, and Cloud Run functions for processing and analytics.
B. Use Pub/Sub for ingestion, Dataflow with Apache Beam for processing, and BigQuery for storage and analytics.
C. Use Cloud Storage for ingestion, Dataproc with Apache Spark for batch processing, and BigQuery for storage and analytics.
D. Use Cloud Data Fusion for ingestion and transformation, and Cloud SQL for storage and analytics.

Answer: B

Explanation:
Comprehensive and Detailed Explanation:
This question requires a solution that excels at large-scale, stateful stream processing with sophisticated windowing and handling of out-of-order data.
Option C is the correct answer because this architecture is perfectly suited for the requirements.
Pub/Sub is the global, scalable ingestion service for continuous event data.
Dataflow, with the Apache Beam programming model, is specifically designed for complex stream processing. It has powerful, built-in support for different windowing strategies (including session windows for sessionization) and sophisticated triggers for handling late-arriving data. Its serverless nature ensures it scales to handle millions of events.
BigQuery is the ideal sink for the processed data, enabling large-scale analytics for the recommendation engine.
Option A is incorrect as Firebase and Cloud Run are more suited for application backends and are not designed for complex, stateful data processing pipelines at this scale.
Option B is incorrect because it describes a batch processing pattern. Using Cloud Storage for ingestion and Dataproc for batch processing would introduce high latency, failing the "low latency" requirement.
Option D is incorrect because Cloud Data Fusion is primarily a batch-oriented ETL/ELT tool, and Cloud SQL is not an analytical data warehouse capable of handling this scale of data for analytics.
Reference (Google Cloud Documentation Concepts):
This is another example of the canonical Pub/Sub -> Dataflow -> BigQuery streaming analytics pattern. The Apache Beam Programming Guide (which is the foundation for Dataflow) extensively covers concepts like Windowing (specifically SessionWindows) and Triggers for handling late data. These features are critical for accurately processing real-world event streams like clickstream data and are core strengths of Dataflow.

NEW QUESTION # 209
Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

A. Threading
B. Dimensionality Reduction
C. Serialization
D. Dropout Methods

Answer: D

Explanation:
Explanation
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505

NEW QUESTION # 210
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