PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Answers

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. Eliminate features that are highly correlated to the output labels.

B. Combine highly co-dependent features into one representative feature.

C. Instead of feeding in each feature individually, average their values in batches of 3.

D. Remove the features that have null values for more than 50% of the training records.

The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

A. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.

B. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.

C. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.

D. Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

A. Get more training examples

B. Reduce the number of training examples

C. Use a smaller set of features

D. Use a larger set of features

E. Increase the regularization parameters

F. Decrease the regularization parameters

You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

A. Use your model to run predictions on fresh customer input data.

B. Test and evaluate your model on your curated data to determine how well the model performs.

C. Monitor your model performance, and make any adjustments needed.

D. Delineate what data will be used for testing and what will be used for training the model.

You need to set access to BigQuery for different departments within your company.

Your solution should comply with the following requirements:

1.

Each department should have access only to their data. Each department will have one or more leads who need to be able to create and update tables and provide them to their team.

2.

Each department has data analysts who need to be able to query but not modify data.

How should you set access to the data in BigQuery?

A. Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset.

B. Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.

C. Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in.

D. Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in.

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

A. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

B. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.

C. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.

D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

Which of the following are feature engineering techniques? (Select 2 answers)

A. Hidden feature layers

B. Feature prioritization

C. Crossed feature columns

D. Bucketization of a continuous feature

When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

A. 500 TB

B. 1 GB

C. 1 TB

D. 500 GB

How can you get a neural network to learn about relationships between categories in a categorical feature?

A. Create a multi-hot column

B. Create a one-hot column

C. Create a hash bucket

D. Create an embedding column

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You've loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.

What should you do?

A. Select random samples from the tables using the RAND() function and compare the samples.

B. Select random samples from the tables using the HASH() function and compare the samples.

C. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

D. Create stratified random samples using the OVER() function and compare equivalent samples from each table.