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Google Professional-Data-Engineer Exam Syllabus Topics:

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

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Google Certified Professional Data Engineer Exam Sample Questions (Q261-Q266):

NEW QUESTION # 261
Which is not a valid reason for poor Cloud Bigtable performance?

A. The workload isn't appropriate for Cloud Bigtable.
B. The Cloud Bigtable cluster has too many nodes.
C. The table's schema is not designed correctly.
D. There are issues with the network connection.

Answer: B

Explanation:
Explanation
The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded, adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION # 262
What are two of the characteristics of using online prediction rather than batch prediction?

A. It is optimized to handle a high volume of data instances in a job and to run more complex models.
B. It is optimized to minimize the latency of serving predictions.
C. Predictions are returned in the response message.
D. Predictions are written to output files in a Cloud Storage location that you specify.

Answer: B,C

Explanation:
Online prediction
Optimized to minimize the latency of serving predictions.
Predictions returned in the response message.
Batch prediction
Optimized to handle a high volume of instances in a job and to run more complex models.
Predictions written to output files in a Cloud Storage location that you specify.
Reference: https://cloud.google.com/ml-engine/docs/prediction-
overview#online_prediction_versus_batch_prediction

NEW QUESTION # 263
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones.
What should you do?

A. Create a table on Cloud Spanner, and insert and delete rows with the job information
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud SQL, and insert and delete rows with the job information
D. Create an API using App Engine to receive and send messages to the applications

Answer: D

Explanation:
References:

NEW QUESTION # 264
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = `1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

A. `bigquery-public-data.noaa_gsod.gsod*`
B. `bigquery-public-data.noaa_gsod.gsod'*
C. bigquery-public-data.noaa_gsod.gsod*
D. `bigquery-public-data.noaa_gsod.gsod`

Answer: A

Explanation:
It follows the correct wildcard syntax of enclosing the table name in backticks and including the * wildcard character.

NEW QUESTION # 265
You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

A. The current epoch time
B. A concatenation of the product name and the current epoch time
C. A random universally unique identifier number (version 4 UUID)
D. The original order identification number from the sales system, which is a monotonically increasing integer

Answer: C

Explanation:
https://cloud.google.com/spanner/docs/schema-and-data-model#choosing_a_primary_key

NEW QUESTION # 266
......

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