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

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Google Data Engineer Sample Questions (Q76-Q81):

NEW QUESTION # 76
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
B. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
C. Merge the transformed tables together with a SQL query
D. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
E. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

Answer: A

NEW QUESTION # 77
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. Use a larger instance type for your Cloud Dataflow workers
B. Create a temporary table in Cloud Spanner that will act as a buffer for new data
C. Create a temporary table in Cloud Bigtable that will act as a buffer for new data
D. Increase the number of max workers
E. 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
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 Bigtable to BigQuery

Answer: A,G

NEW QUESTION # 78
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
D. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Answer: C

NEW QUESTION # 79
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. Cloud SQL
B. Cloud Datastore
C. Cloud Bigtable
D. BigQuery

Answer: D

NEW QUESTION # 80
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 # 81
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