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Google Data Engineer Sample Questions:

1. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
B) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
C) Partition the tables by a column containing a TIMESTAMP or DATE Type.
D) Re-create the tables using DD
E) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
F) Write an Apache Beam pipeline that creates a BigQuery table per 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 AutoML with the existing dataset.
B) Use Cloud Vision AutoML, but reduce your dataset twice.
C) Train your own image recognition model leveraging transfer learning techniques.
D) Use Cloud Vision API by providing custom labels as recognition hints.

3. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
B) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
C) Add a SideInput that returns a Boolean if the element is corrupt.
D) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

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

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

5. You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A) Then run the Hadoop utility to copy them do HDF
B) Replicate external Hive tables to the native ones.
C) Mount the Hive tables locally.
D) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
E) Mount the Hive tables from HDFS.
F) Mount the Hive tables locally.
G) Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
H) Replicate external Hive tables to the native ones.
I) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
J) Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
K) Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
L) Load the ORC files into BigQuer

Solutions: