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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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 lake architecture on Google Cloud Storage 2. Data warehouse design using BigQuery
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
Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Logging and observability 2. Performance optimization and debugging - Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines
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. ETL/ELT workflows 2. Pub/Sub streaming ingestion

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Google Data Engineer Sample Questions (Q49-Q54):

NEW QUESTION # 49
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?

A. ParDo
B. Sink API
C. Source API
D. Data extraction

Answer: A

Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do

NEW QUESTION # 50
Cloud Dataproc is a managed Apache Hadoop and Apache service.

A. Ignite
B. Blaze
C. Fire
D. Spark

Answer: D

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

NEW QUESTION # 51
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
B. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D. Add a SideInput that returns a Boolean if the element is corrupt.

Answer: B

NEW QUESTION # 52
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A. Increase the number of max workers
B. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
C. Create a temporary table in Cloud Spanner that will act as a buffer for new data
D. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
E. Use a larger instance type for your Cloud Dataflow workers
F. Change the zone of your Cloud Dataflow pipeline to run in us-central1
G. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

Answer: B,E

NEW QUESTION # 53
You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

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

Answer: A

NEW QUESTION # 54
......

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