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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q30-Q35):

NEW QUESTION # 30
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. Merge the transformed tables together with a SQL query
C. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D. 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
E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
F. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme

Answer: A

NEW QUESTION # 31
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create a cron schedule in Cloud Dataprep.
B. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
C. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
D. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Answer: D

NEW QUESTION # 32
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 # 33
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
B. Load the data every 30 minutes into a new partitioned table in BigQuery.
C. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
D. Store the data in Google Cloud Datastor
E. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
F. Store the data in a file in a regional Google Cloud Storage bucke

Answer: B

NEW QUESTION # 34
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. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
C. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
D. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Answer: C

NEW QUESTION # 35
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