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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

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

2. You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A) Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
B) In the event of an emergency, use the backup copy of the table.
C) Set the BigQuery dataset to be regiona
D) In the event of an emergency, use the backup copy of the table.
E) In the event of an emergency, use a point-in-time snapshot to recover the data.
F) Set the BigQuery dataset to be regiona
G) Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
H) Set the BigQuery dataset to be multi-regional
I) Set the BigQuery dataset to be multi-regional
J) In the event of an emergency, use a point-in-time snapshot to recover the data.

3. You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A) Transform
B) Sink API
C) PCollection
D) Pipeline

4. Which is not a valid reason for poor Cloud Bigtable performance?

A) There are issues with the network connection.
B) The Cloud Bigtable cluster has too many nodes.
C) The table's schema is not designed correctly.
D) The workload isn't appropriate for Cloud Bigtable.

5. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Call the model from your application and process the results as labels.
B) Call the Cloud Natural Language API from your applicatio
C) Deploy the model using a KubernetesEngine cluste
D) Process the generated Sentiment Analysis as labels.
E) Deploy the model using Cloud Machine Learning Engin
F) Call the model from your application and process the results as labels.
G) Call the Cloud Natural Language API from your applicatio
H) Process the generated Entity Analysis as labels.
I) Build and train a text classification model using TensorFlo
J) Build and train a text classification model using TensorFlo

Solutions: