Google Professional-Data-Engineer Certification Exam Dumps with 253 Practice Test Questions [Q55-Q70]

NO.55 You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?

Disable caching by editing the report settings.

Disable caching in BigQuery by editing table details.

Refresh your browser tab showing the visualizations.

Clear your browser history for the past hour then reload the tab showing the virtualizations.

NO.56 Which of these are examples of a value in a sparse vector? (Select 2 answers.)

[0, 5, 0, 0, 0, 0]

[0, 0, 0, 1, 0, 0, 1]

[0, 1]

[1, 0, 0, 0, 0, 0, 0]

NO.57 You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

Increase the number of max workers

Use a larger instance type for your Cloud Dataflow workers

Change the zone of your Cloud Dataflow pipeline to run in us-central1

Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery

NO.58 You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

Create a cron schedule in Cloud Dataprep.

Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

NO.59 Your company is in a highly regulated industry. One of your requirements is to ensure individual users
have access only to the minimum amount of information required to do their jobs. You want to enforce this
requirement with Google BigQuery. Which three approaches can you take? (Choose three.)

Disable writes to certain tables.

Restrict access to tables by role.

Ensure that the data is encrypted at all times.

Restrict BigQuery API access to approved users.

Segregate data across multiple tables or databases.

Use Google Stackdriver Audit Logging to determine policy violations.

NO.60 Which methods can be used to reduce the number of rows processed by BigQuery?

Splitting tables into multiple tables; putting data in partitions

Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause

Putting data in partitions; using the LIMIT clause

Splitting tables into multiple tables; using the LIMIT clause

NO.61 You are developing an application on Google Cloud that will automatically generate subject labels for users’ blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.

Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.

Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.

Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.

NO.62 An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

NO.63 Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible.
They also want to be able to persist data beyond the life of the cluster. What should you do?

Create a Google Cloud Dataflow job to process the data.

Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

Create a Hadoop cluster on Google Compute Engine that uses persistent disks.

Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.

Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

NO.64 Which of the following is NOT true about Dataflow pipelines?

Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

Dataflow pipelines can consume data from other Google Cloud services

Dataflow pipelines can be programmed in Java

Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

NO.65 The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

Cloud Dataflow connector

DataFlow SDK

BiqQuery API

BigQuery Data Transfer Service

NO.66 You operate a logistics company, and you want to improve event delivery reliability for vehicle-based sensors.
You operate small data centers around the world to capture these events, but leased lines that provide connectivity from your event collection infrastructure to your event processing infrastructure are unreliable, with unpredictable latency. You want to address this issue in the most cost-effective way. What should you do?

Deploy small Kafka clusters in your data centers to buffer events.

Have the data acquisition devices publish data to Cloud Pub/Sub.

Establish a Cloud Interconnect between all remote data centers and Google.

Write a Cloud Dataflow pipeline that aggregates all data in session windows.

NO.67 When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

zone

node

label

type

NO.68 You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour.
Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

NO.69 You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’the property ‘actors’and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

Manually configure the index in your index config as follows:

Set the following in your entity options: exclude_from_indexes = ‘actors, tags’

Set the following in your entity options: exclude_from_indexes = ‘date_published’

NO.70 An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

BigQuery

Cloud SQL

Cloud BigTable

Cloud Datastore

Google Professional-Data-Engineer Certification Exam Dumps with 253 Practice Test Questions [Q55-Q70]

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