PDE sample questions

Question 1

You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern.
Which service do you select for storing and serving your data?

Accepted Answer

Cloud SQL

Answer

Cloud Firestore

Answer

Cloud Bigtable

Answer

Cloud Spanner

Question 2

You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

Accepted Answer

Create authorized views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the authorized views

Answer

Assign the users/groups data viewer access at the table level for each table

Answer

Create authorized views for each team in datasets created for each team. Assign the authorized views data viewer access to the dataset in which the data resides. Assign the users/groups data viewer access to the datasets in which the authorized views reside

Answer

Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views

Question 3

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

Accepted Answer

Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Answer

Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Answer

Increase the cluster size with more non-preemptible workers.

Answer

Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Question 4

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

Accepted Answer

Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Answer

Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Answer

set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Answer

Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

Question 5

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Accepted Answer

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Answer

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Answer

Load the data every 30 minutes into a new partitioned table in BigQuery.

Answer

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

Question 6

You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

Accepted Answer

Use the LIMIT keyword to reduce the number of rows returned.

Answer

Recreate the table with a partitioning column and clustering column.

Answer

Create a separate table for each ID.

Answer

Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.

Question 7

Your company has hired a new data scientist who wants to perform complicated analyses across very large datasets stored in Google Cloud Storage and in a Cassandra cluster on Google Compute Engine. The scientist primarily wants to create labelled data sets for machine learning projects, along with some visualization tasks. She reports that her laptop is not powerful enough to perform her tasks and it is slowing her down. You want to help her perform her tasks. What should you do?

Accepted Answer

Grant the user access to Google Cloud Shell.

Answer

Deploy Google Cloud Datalab to a virtual machine (VM) on Google Compute Engine.

Answer

Run a local version of Jupiter on the laptop.

Answer

Host a visualization tool on a VM on Google Compute Engine.

Question 8

You are developing an application that uses a recommendation engine on Google Cloud. Your solution should display new videos to customers based on past views. Your solution needs to generate labels for the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering suggestions based on data from other customer preferences on several TB of data. What should you do?

Accepted Answer

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user’s viewing history to generate preferences.

Answer

Build and train a classification model with Spark MLlib to generate labels. Build and train a second classification model with Spark MLlib to filter results to match customer preferences.
Deploy the models using Cloud Dataproc. Call the models from your application.

Answer

Build and train a complex classification model with Spark MLlib to generate labels and filter the results. Deploy the models using Cloud Dataproc. Call the model from your application.

Answer

Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud SQL, and join and filter the predicted labels to match the user’s viewing history to generate preferences.

Question 9

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

Accepted Answer

Retry the query with exponential backoff, up to a cap of 15 minutes.

Answer

issue a command to restart the database servers.

Answer

Retry the query every second until it comes back online to minimize staleness of data.

Answer

Reduce the query frequency to once every hour until the database comes back online.

Question 10

You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps.
You have the following requirements:
You will batch-load the posts once per day and run them through the Cloud Natural Language API.
You will extract topics and sentiment from the posts.
You must store the raw posts for archiving and reprocessing.
You will create dashboards to be shared with people both inside and outside your organization.
You need to store both the data extracted from the API to perform analysis as well as ther aw social media posts for historical archiving. What should you do?

Accepted Answer

Feed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.

Answer

Store the social media posts and the data extracted from the API in Cloud SQL.

Answer

Store the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.

Answer

Store the social media posts and the data extracted from the API in BigQuery.

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Free PDE sample questions

You need to migrate a 2TB relational database to Google Cloud Platform. You do not have the resources to significantly refactor the application that uses this database and cost to operate is of primary concern. Which service do you select for storing and serving your data?

You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

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