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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Maintaining and optimizing data and ML solutions	20%	- Security and governance 1. IAM and access control 2. Data encryption and compliance - Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines
Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion - Data pipeline implementation 1. Dataflow pipeline development 2. Dataproc and Spark-based processing
Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Scheduling and monitoring pipelines 2. Cloud Composer workflows - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
Designing data processing systems	20%	- Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage - Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design

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Google Data Engineer Sample Questions (Q68-Q73):

NEW QUESTION # 68
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can be programmed in Java
B. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
C. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources
D. Dataflow pipelines can consume data from other Google Cloud services

Answer: B

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 69
You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A. Use Cloud Vision API by providing custom labels as recognition hints.
B. Use Cloud Vision AutoML with the existing dataset.
C. Train your own image recognition model leveraging transfer learning techniques.
D. Use Cloud Vision AutoML, but reduce your dataset twice.

Answer: B

NEW QUESTION # 70
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
B. Consume the stream of data in Cloud Dataflow using Kafka I
C. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
E. If that number falls below 4000, send an alert.
F. Consume the stream of data in Cloud Dataflow using Kafka I
G. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
I. If that number falls below 4000, send an alert.
J. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
K. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
L. Set a sliding time window of 1 hour every 5 minute
M. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour

Answer: C

NEW QUESTION # 71
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

A. BigQuery and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Storage
D. Cloud Datastore and Cloud Bigtable

Answer: A

NEW QUESTION # 72
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A. Change "person" to "city.person".
B. Add ", UNNEST(city)" before the WHERE clause.
C. Add ", UNNEST(person)" before the WHERE clause.
D. Change "person" to "person.city".

Answer: C

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

NEW QUESTION # 73
......

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