Free Access Google.Professional-Data-Engineer.v2022-07-07.q155 Practice Test (Page 6)

Question 21

You work for a manufacturing plant that batches application log files together into a single log file once a day at
2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

A.Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

B.Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

C.Manually start the Cloud Dataflow job each morning when you get into the office.

D.Change the processing job to use Google Cloud Dataproc instead.

Question 22

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last
2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A.Rowkey: date#data_pointColumn data: device_id

B.Rowkey: dateColumn data: device_id, data_point

C.Rowkey: device_idColumn data: date, data_point

D.Rowkey: data_pointColumn data: device_id, date

E.Rowkey: date#device_idColumn data: data_point

Question 23

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?

A.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.

B.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.

C.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.

D.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.

Question 24

To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

A.gcloud ml-engine local train

B.gcloud ml-engine jobs submit training

C.gcloud ml-engine jobs submit training local

D.You can't run a TensorFlow program on your own computer using Cloud ML Engine .

Question 25

You are designing storage for 20 TB of text files as part of deploying a data pipeline on Google Cloud. Your input data is in CSV format. You want to minimize the cost of querying aggregate values for multiple users who will query the data in Cloud Storage with multiple engines. Which storage service and schema design should you use?

A.Use Cloud Storage for storage. Link as temporary tables in BigQuery for query.

B.Use Cloud Storage for storage. Link as permanent tables in BigQuery for query.

C.Use Cloud Bigtable for storage. Link as permanent tables in BigQuery for query.

D.Use Cloud Bigtable for storage. Install the HBase shell on a Compute Engine instance to query the Cloud Bigtable data.

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