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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. If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?
A) Use at least 300 GB of data.
B) Do not use a production instance.
C) Before you test, run a heavy pre-test for several minutes.
D) Run your test for at least 10 minutes.
3. You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?
A) Sink API
B) Data extraction
C) ParDo
D) Source API
4. Which of these is not a supported method of putting data into a partitioned table?
A) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
B) 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".
C) 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.
D) Create a partitioned table and stream new records to it every day.
5. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?
A) Cloud Datastore
B) Cloud Bigtable
C) Cloud SQL
D) BigQuery
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |
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