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

2. Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A) A Dataproc cluster cannot have only preemptible workers.
B) Preemptible workers cannot use persistent disk.
C) Preemptible workers cannot store data.
D) If a preemptible worker is reclaimed, then a replacement worker must be added manually.

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

4. Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.
B) Use K-means Clustering to detect faces in the pixels.
C) Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D) Use feature engineering to add features for eyes, noses, and mouths to the input data.

5. 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 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
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 a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
D) Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project

Solutions: