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Google Professional-Data-Engineer (Google Certified Professional Data Engineer) certification exam is designed for individuals who have expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Professional-Data-Engineer Exam is intended for professionals who want to validate their skills and knowledge in data engineering, including the design and implementation of scalable and robust data processing systems.

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The Google Professional-Data-Engineer Exam for the Google Professional-Data-Engineer certification is a comprehensive and challenging test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions, which require candidates to apply their knowledge to real-world scenarios. Candidates are required to demonstrate their expertise in areas such as data processing, data analysis, data integration, and data visualization.

Google Certified Professional Data Engineer Exam Sample Questions (Q221-Q226):

NEW QUESTION # 221
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)

A. Introduce resource hierarchy to leverage access control policy inheritance.
B. Use Cloud Deployment Manager to automate access provision.
C. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Answer: B,E

NEW QUESTION # 222
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules.
You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A. Create an authorized view in BigQuery to restrict access to tables with sensitive data.
B. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
C. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
D. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Answer: D

NEW QUESTION # 223
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. Serialization
B. Dimensionality Reduction
C. Dropout Methods
D. Threading

Answer: C

Explanation:
Explanation/Reference:
Reference: https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-
tensorflow-30505541d877

NEW QUESTION # 224
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. 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.
B. Implement a Cloud Composer DAG, with each step defined as a separate task using appropriate Airflow operators, and schedule the DAG for daily execution.
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. 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: B

Explanation:
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.

NEW QUESTION # 225
You want to create a machine learning model using BigQuery ML and create an endpoint for hosting the model using Vertex AI. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

A. Create a new BigQuery dataset and use streaming inserts to land the data from multiple vendors.
Configure your BigQuery ML model to use the "ingestion" dataset as the framing data.
B. Create a Pub/Sub topic and send all vendor data to it. Use Dataflow to process and sanitize the Pub/ Sub data and stream it to BigQuery.
C. Create a Pub/Sub topic and send all vendor data to it. Connect a Cloud Function to the topic to process the data and store it in BigQuery.
D. Use BigQuery streaming inserts to land the data from multiple vendors where your BigQuery dataset ML model is deployed.

Answer: B

NEW QUESTION # 226
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