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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Building and operationalizing data processing systems	30%	- Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development - Data ingestion and transformation 1. Pub/Sub streaming ingestion 2. ETL/ELT workflows
Topic 2: Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines - Security and governance 1. Data encryption and compliance 2. IAM and access control
Topic 3: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines - Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging
Topic 4: Designing data processing systems	20%	- Data pipeline architecture design 1. Scalable data ingestion design 2. Batch vs streaming data processing selection - Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage

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Google Data Engineer Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. Preemptible workers cannot store data.
B. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
C. A Dataproc cluster cannot have only preemptible workers.
D. Preemptible workers cannot use persistent disk.

Answer: A,C

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 15
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A. Use the diagnose command to generate an operational output archiv
B. Use Cloud Dataproc to run your transformation
C. Use Cloud Dataflow to run your transformation
D. Monitor the job system lag with Stackdrive
E. Configure the job to use non-default Compute Engine machine types when needed.
F. Use the default autoscaling setting for worker instances.
G. Use Cloud Dataflow to run your transformation
H. Resize the number of worker nodes in your cluster via the command line.
I. Use Cloud Dataproc to run your transformation
J. Locate the bottleneck and adjust cluster resources.
K. Monitor CPU utilization for the cluste
L. Monitor the total execution time for a sampling of job

Answer: K

NEW QUESTION # 16
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. Cloud Speech-to-Text API
B. Cloud Natural Language API
C. Dialogflow Enterprise Edition
D. Cloud AutoML Natural Language

Answer: D

NEW QUESTION # 17
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A. Pipeline
B. Transform
C. PCollection
D. Sink API

Answer: B

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model

NEW QUESTION # 18
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. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
B. Create a table on Cloud SQL, and insert and delete rows with the job information
C. Create an API using App Engine to receive and send messages to the applications
D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: A

NEW QUESTION # 19
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