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Google Data Engineer Sample Questions:

1. You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A) Remove the features that have null values for more than 50% of the training records.
B) Eliminate features that are highly correlated to the output labels.
C) Combine highly co-dependent features into one representative feature.
D) Instead of feeding in each feature individually, average their values in batches of 3.

2. 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

3. 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) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
C) Use BigQuery'ssupport for external data sources to query.
D) Use BigQuery UPDATE to further reduce the size of the dataset.
E) Preserve the structure of the data as much as possible.
F) Denormalize the data as must as possible.

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) Cloud Scheduler
C) cron
D) Workflow Templates on Cloud Dataproc

5. You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A) Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B) Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
C) Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
D) Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

Solutions: