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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
B) Preserve the structure of the data as much as possible.
C) Use BigQuery UPDATE to further reduce the size of the dataset.
D) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
E) Use BigQuery'ssupport for external data sources to query.
F) Denormalize the data as must as possible.

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

3. Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A) The CSV data loaded in BigQuery is not flagged as CSV.
B) The CSV data has invalid rows that were skipped on import.
C) The CSV data loaded in BigQuery is not using BigQuery's default encoding.
D) The CSV data has not gone through an ETL phase before loading into BigQuery.

4. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A) Cloud Composer
B) cron
C) Workflow Templates on Cloud Dataproc
D) Cloud Scheduler

5. As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A) Introduce resource hierarchy to leverage access control policy inheritance.
B) Use Cloud Deployment Manager to automate access provision.
C) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
E) Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
F) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.

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