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Google Data Engineer Sample Questions:

1. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A) Use the WHERE_PARTITIONTIME pseudo column
B) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
C) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD
D) Use the TABLE_DATE_RANGE function

2. 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) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
D) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

3. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A) Increase the number of nodes in the Cloud Bigtable cluster
B) Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
C) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
D) Configure your Cloud Dataflow pipeline to use local execution
E) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

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

5. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A) Cloud Pub/Sub, Cloud Dataflow, and Local SSD
B) Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
C) Cloud Pub/Sub, Cloud SQL, and Cloud Storage
D) Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

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