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Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Ensuring solution quality	28%	- Reliability and performance 1. Fault tolerance and recovery strategies 2. Monitoring pipelines and workloads - Security and governance 1. Data governance and compliance 2. IAM and access control in GCP
Building and operationalizing data processing systems	24%	- Data processing and transformation 1. ETL/ELT pipeline design 2. Using Dataproc, Dataflow, and BigQuery SQL - Data ingestion and integration 1. Batch ingestion pipelines (BigQuery, Cloud Storage) 2. Streaming ingestion (Pub/Sub, Dataflow)
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
Operationalizing machine learning models	26%	- ML pipeline integration 1. Vertex AI pipeline deployment 2. Feature engineering and feature stores - Model deployment and monitoring 1. Online vs batch prediction 2. Model monitoring and drift detection

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Google Certified Professional Data Engineer Exam Sample Questions (Q253-Q258):

NEW QUESTION # 253
You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. 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 BigOuery dataset and use streaming inserts to land the data from multiple vendors. Configure your BigQuery ML model to use the "ingestion' dataset as the training 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 whore your BigQuery dataset ML model is deployed.

Answer: B

Explanation:
Dataflow provides a scalable and flexible way to process and clean the incoming data in real-time before loading it into BigQuery.

NEW QUESTION # 254
Your organization's data assets are stored in BigQuery, Pub/Sub, and a PostgreSQL instance running on Compute Engine. Because there are multiple domains and diverse teams using the data, teams in your organization are unable to discover existing data assets. You need to design a solution to improve data discoverability while keeping development and configuration efforts to a minimum. What should you do?

A. Use Data Catalog to automatically catalog BigQuery datasets. Use Data Catalog APIs to manually catalog Pub/Sub topics and PostgreSQL tables.
B. Use customer connectors to manually catalog BigQuery datasets, Pub/Sub topics, and PostgreSQL tables.
C. Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use custom connectors to manually catalog PostgreSQL tables.
D. Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use Data Catalog APIs to manually catalog PostgreSQL tables.

Answer: C

NEW QUESTION # 255
An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

A. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format
B. Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source
C. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format
D. Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used

Answer: C

NEW QUESTION # 256
An aerospace company uses a proprietary data format to store its night dat
a. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

A. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format
B. Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source
C. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format
D. Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used

Answer: C

NEW QUESTION # 257
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
B. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
C. Add capacity (memory and disk space) to the database server by the order of 200.
D. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.

Answer: A

NEW QUESTION # 258
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