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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Designing data processing systems	22%-27%	- Designing data pipelines 1. Build ETL and ELT pipelines 2. Design orchestration workflows 3. Optimize pipeline performance and cost - Designing for data ingestion 1. Design scalable and reliable ingestion architecture 2. Design batch and streaming ingestion solutions 3. Select appropriate storage systems
Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Implement batch processing systems 2. Implement streaming data systems 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services - Operationalizing systems 1. Implement fault tolerance and recovery 2. Manage logging and alerting 3. Monitor data pipelines and workloads
Ensuring solution quality	20%-25%	- Security and compliance 1. Manage IAM and access control 2. Protect sensitive data 3. Implement encryption and compliance controls - Data quality management 1. Implement data governance 2. Validate and clean data 3. Maintain metadata and lineage
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Control operational costs 2. Optimize storage and compute usage 3. Tune query and pipeline performance - Reliability and scalability 1. Design highly available systems 2. Implement disaster recovery strategies 3. Scale data workloads efficiently

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Google Data Engineer Sample Questions (Q14-Q19):

NEW QUESTION # 14
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
B. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Deploy the Cloud SQL Proxy on the Cloud Dataproc master

Answer: A

NEW QUESTION # 15
You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A. Create separate views to cover each month, and query from these views
B. Convert the sharded tables into a single partitioned table
C. Enable query caching so you can cache data from previous months
D. Convert all daily log tables into date-partitioned tables

Answer: D

NEW QUESTION # 16
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. Data extraction
B. ParDo
C. Source API
D. Sink API

Answer: B

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 # 17
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 GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Answer: C

NEW QUESTION # 18
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 temporary table in Cloud Spanner that will act as a buffer for new data
C. Change the zone of your Cloud Dataflow pipeline to run in us-central1
D. Use a larger instance type for your Cloud Dataflow workers
E. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
F. 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
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: D,F

NEW QUESTION # 19
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