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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Designing data processing systems	22%-27%	- Designing data pipelines 1. Optimize pipeline performance and cost 2. Design orchestration workflows 3. Build ETL and ELT pipelines - Designing for data ingestion 1. Select appropriate storage systems 2. Design batch and streaming ingestion solutions 3. Design scalable and reliable ingestion architecture
Topic 2: Ensuring solution quality	20%-25%	- Security and compliance 1. Implement encryption and compliance controls 2. Manage IAM and access control 3. Protect sensitive data - Data quality management 1. Implement data governance 2. Validate and clean data 3. Maintain metadata and lineage
Topic 3: Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Tune query and pipeline performance 2. Optimize storage and compute usage 3. Control operational costs - Reliability and scalability 1. Implement disaster recovery strategies 2. Design highly available systems 3. Scale data workloads efficiently
Topic 4: Building and operationalizing data processing systems	28%-33%	- Operationalizing systems 1. Implement fault tolerance and recovery 2. Manage logging and alerting 3. Monitor data pipelines and workloads - Building data processing systems 1. Implement batch processing systems 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 3. Implement streaming data systems

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Google Data Engineer Sample Questions (Q26-Q31):

NEW QUESTION # 26
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: device_idColumn data: date, data_point
B. Rowkey: dateColumn data: device_id, data_point
C. Rowkey: date#data_pointColumn data: device_id
D. Rowkey: date#device_idColumn data: data_point
E. Rowkey: data_pointColumn data: device_id, date

Answer: E

NEW QUESTION # 27
What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and exporting data
B. Queries and streaming inserts
C. Storage, queries, and streaming inserts
D. Storage, queries, and loading data from a file

Answer: C

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 28
You are implementing several batch jobs that must be executed on a schedule. These jobs have many interdependent steps that must be executed in a specific order. Portions of the jobs involve executing shell scripts, running Hadoop jobs, and running queries in BigQuery. The jobs are expected to run for many minutes up to several hours. If the steps fail, they must be retried a fixed number of times. Which service should you use to manage the execution of these jobs?

A. Cloud Composer
B. Cloud Scheduler
C. Cloud Functions
D. Cloud Dataflow

Answer: B

NEW QUESTION # 29
You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application.
What should you do?

A. Create a service account and grant dataset access to that accoun
B. Create groups for your users and give those groups access to the dataset
C. Use the service account's private key to access the dataset
D. Create a dummy user and grant dataset access to that use
E. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset
F. Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request

Answer: A

NEW QUESTION # 30
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 ParDo transform in Cloud Dataflow to discard corrupt elements.
B. Add a SideInput that returns a Boolean if the element is corrupt.
C. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Answer: A

NEW QUESTION # 31
......

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