Exam Name: Data Engineer
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Preparing data for analysis and machine learning | 13% | - Enabling data analysis
|
| Storing and managing data | 20% | - Implementing storage solutions
|
| Maintaining and automating data workloads | 18% | - Automation and optimization
|
| Designing data processing systems | 24% | - Designing data pipelines
|
| Ingesting and processing data | 25% | - Building data pipelines
|
Google Data Engineer Sample Questions:
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?
- A. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
- B. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
- C. Use BigQuery to export the data for the table to a CSV fil
- D. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
- E. Add a new column called FullName to the Users tabl
- F. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
Correct Answer: B 🗳️
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
- B. Create an API using App Engine to receive and send messages to the applications
- C. Create a table on Cloud Spanner, and insert and delete rows with the job information
- D. Create a table on Cloud SQL, and insert and delete rows with the job information
Correct Answer: A 🗳️
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 slots/allocated_for_project
- B. 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
- 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 Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
Correct Answer: C 🗳️
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. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
- 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. Merge the transformed tables together with a SQL query
- F. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
Correct Answer: F 🗳️
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?
- A. A non-sequential numeric ID
- B. A stock symbol followed by a timestamp
- C. A timestamp followed by a stock symbol
- D. A sequential numeric ID
Correct Answer: C,D 🗳️
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