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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	28%	- Reliability and performance 1. Monitoring pipelines and workloads 2. Fault tolerance and recovery strategies - Security and governance 1. Data governance and compliance 2. IAM and access control in GCP
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
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 Sample Questions (Q55-Q60):

NEW QUESTION # 55
Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

A. Maintain a database table to store the hash value and other metadata for each data entry.
B. Store each data entry as the primary key in a separate database and apply an index.
C. Compute the hash value of each data entry, and compare it with all historical data.
D. Assign global unique identifiers (GUID) to each data entry.

Answer: A

NEW QUESTION # 56
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Dialogflow Enterprise Edition
B. Cloud Speech-to-Text API
C. Cloud AutoML Natural Language
D. Cloud Natural Language API

Answer: C

NEW QUESTION # 57
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?

A. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
B. Export the information to Cloud Stackdriver, and set up an Alerting policy
C. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs
D. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver

Answer: D

NEW QUESTION # 58
You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 3090 days of data, the query scans the entire table.
You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries.
What should you do?

A. Write an Apache Beam pipeline that creates a BigQuery table per day. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
B. Re-create the tables using DDL. Partition the tables by a column containing a TIMESTAMP or DATE Type.
C. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
D. Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.

Answer: B

NEW QUESTION # 59
You are preparing data to serve a sales demand prediction model. The training data undergoes several pre-processing steps, including scaling numerical features and one-hot encoding categorical features. The model is deployed on Vertex AI Endpoints. You need to prevent training-serving skew and ensure accurate predictions in production. You want a solution that is easy to implement. What should you do?

A. Store the raw, unprocessed data in a separate Cloud Storage bucket exclusively for serving.
B. Replicate the exact same pre-processing logic in the inference pipeline that was used during model training.
C. Implement a custom handler within the Vertex AI Endpoint to automatically perform data transformations before the model makes a prediction.
D. Ensure the serving data is a smaller, random sample of the training data.

Answer: B

Explanation:
Replicating the exact pre-processing logic from training in the inference pipeline ensures consistency between training and serving, preventing training-serving skew and maintaining prediction accuracy without adding unnecessary complexity.

NEW QUESTION # 60
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