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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q61-Q66):

NEW QUESTION # 61
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 GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C. Add a SideInput that returns a Boolean if the element is corrupt.
D. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

Answer: A

NEW QUESTION # 62
You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
B. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
C. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.
D. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

Answer: B

NEW QUESTION # 63
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D. Cloud Pub/Sub, Cloud SQL, and Cloud Storage

Answer: D

NEW QUESTION # 64
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
B. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
C. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
D. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.

Answer: D

NEW QUESTION # 65
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 # 66
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