Professional-Data-Engineer Google Professional Data Engineer Exam exact Exam Questions

Google Professional Data Engineer Exam

Last Update 13 hours ago Total Questions : 400

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Question # 51

You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)

Configure your Cloud Dataflow pipeline to use local execution

Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

Increase the number of nodes in the Cloud Bigtable cluster

Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable

Question # 52

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have

asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

Increase the CPU size on your server.

Increase the size of the Google Persistent Disk on your server.

Increase your network bandwidth from your datacenter to GCP.

Increase your network bandwidth from Compute Engine to Cloud Storage.

Question # 53

You have a Standard Tier Memorystore for Redis instance deployed in a production environment. You need to simulate a Redis instance failover in the most accurate disaster recovery situation, and ensure that the failover has no impact on production data. What should you do?

Create a Standard Tier Memorystore for Redis instance in a development environment. Initiate a manual failover by using the force-data-loss data protection mode.

Initiate a manual failover by using the limited-data-loss data protection mode to the Memorystore for Redis instance in theproduction environment.

Increase one replica to Redis instance in production environment. Initiate a manual failover by using the force-data-loss dataprotection mode.

Create a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-data-loss data protection mode.

Question # 54

You issue a new batch job to Dataflow. The job starts successfully, processes a few elements, and then suddenly fails and shuts down. You navigate to the Dataflow monitoring interface where you find errors related to a particular DoFn in your pipeline. What is the most likely cause of the errors?

Exceptions in worker code

Job validation

Graph or pipeline construction

Insufficient permissions

Question # 55

You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards for example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding What should you do?

Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.

Use BigQuery and GoogleSQL to normalize the data, and schedule recurring quenes in BigQuery.

Create a Spark job and submit it to Dataproc Serverless.

Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.

Question # 56

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?

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

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

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

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

Question # 57

You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

Use Dataflow Prime, and enable Right Fitting to increase the worker resources.

Update the job to increase the maximum number of workers.

Enable Vertical Autoscaling to let the pipeline use larger workers.

Change the pipeline code, and introduce a Reshuffle step to prevent fusion.

Question # 58

You are building a model to make clothing recommendations. You know a user’s fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available. How should you use this data to train the model?

Continuously retrain the model on just the new data.

Continuously retrain the model on a combination of existing data and the new data.

Train on the existing data while using the new data as your test set.

Train on the new data while using the existing data as your test set.

Question # 59

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.

Question # 60

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.