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The Google Professional-Data-Engineer Exam for the Google Professional-Data-Engineer certification is a comprehensive and challenging test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions, which require candidates to apply their knowledge to real-world scenarios. Candidates are required to demonstrate their expertise in areas such as data processing, data analysis, data integration, and data visualization.

Google Certified Professional Data Engineer Exam Sample Questions (Q298-Q303):

NEW QUESTION # 298
If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

A. Unsupervised learning
B. Classifier
C. Clustering estimator
D. Regressor

Answer: D

Explanation:
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.

NEW QUESTION # 299
You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on- premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
B. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
C. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
D. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Answer: B

Explanation:
Cloud composer is used to schedule the interdependent jobs.

NEW QUESTION # 300
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time.
This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

A. Implement clustering in BigQuery on the ingest date column.
B. Tier older data onto Cloud Storage files, and leverage extended tables.
C. Implement clustering in BigQuery on the package-tracking ID column.
D. Re-create the table using data partitioning on the package delivery date.

Answer: C

NEW QUESTION # 301
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?

A. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
B. 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.
C. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
D. 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: C

Explanation:
highly available = multi-regional:
https://cloud.google.com/bigquery/docs/locations
recovery strategy of this data that minimizes cost = point-in-time snapshot:
https://cloud.google.com/solutions/bigquery-data-warehouse#backup-and-recovery

NEW QUESTION # 302
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

A. Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.
B. Specify a worker region by using the -region flag.
C. Set the pipeline staging location as a regional Cloud Storage bucket.
D. Submit duplicate pipelines in two different zones by using the -zone flag.

Answer: B

Explanation:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case of zonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the
-zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. References:
* 1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
* 3: Regional endpoints | Cloud Dataflow | Google Cloud

NEW QUESTION # 303
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