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Google Data Engineer Sample Questions:

1. When a Cloud Bigtable node fails, is lost.

A) the last transaction
B) the time dimension
C) all data
D) no data

2. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A) Use Cloud Vision API by providing custom labels as recognition hints.
B) Use Cloud Vision AutoML with the existing dataset.
C) Use Cloud Vision AutoML, but reduce your dataset twice.
D) Train your own image recognition model leveraging transfer learning techniques.

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) Preserve the structure of the data as much as possible.
B) Denormalize the data as must as possible.
C) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
D) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
E) Use BigQuery'ssupport for external data sources to query.
F) Use BigQuery UPDATE to further reduce the size of the dataset.

4. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A) HTTPS
B) HTTP
C) SOCKS
D) VPN

5. Which of these is not a supported method of putting data into a partitioned table?

A) Create a partitioned table and stream new records to it every day.
B) If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
C) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D) Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

Solutions: