Professional-Data-Engineer Exam Dumps - Google Professional Data Engineer Exam

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Question # 25

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

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Question # 26

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?

Issue a command to restart the database servers.

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

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

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

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Question # 27

You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain usersâ€™ privacy?

Grant the consultant the Viewer role on the project.

Grant the consultant the Cloud Dataflow Developer role on the project.

Create a service account and allow the consultant to log on with it.

Create an anonymized sample of the data for the consultant to work with in a different project.

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Question # 28

You work for a car manufacturer and have set up a data pipeline using Google Cloud Pub/Sub to capture anomalous sensor events. You are using a push subscription in Cloud Pub/Sub that calls a custom HTTPS endpoint that you have created to take action of these anomalous events as they occur. Your custom HTTPS endpoint keeps getting an inordinate amount of duplicate messages. What is the most likely cause of these duplicate messages?

The message body for the sensor event is too large.

Your custom endpoint has an out-of-date SSL certificate.

The Cloud Pub/Sub topic has too many messages published to it.

Your custom endpoint is not acknowledging messages within the acknowledgement deadline.

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Question # 29

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?

Eliminate features that are highly correlated to the output labels.

Combine highly co-dependent features into one representative feature.

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

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

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Question # 30

Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

Set a single global window to capture all the data.

Set sliding windows to capture all the lagged data.

Use watermarks and timestamps to capture the lagged data.

Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

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Question # 31

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

Include ORDER BY DESK on timestamp column and LIMIT to 1.

Use GROUP BY on the unique ID column and timestamp column and SUM on the values.

Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.

Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

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Question # 32

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

Assign global unique identifiers (GUID) to each data entry.

Compute the hash value of each data entry, and compare it with all historical data.

Store each data entry as the primary key in a separate database and apply an index.

Maintain a database table to store the hash value and other metadata for each data entry.

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