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Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

ML APIs (e.g., Vision API, Speech API)
Conversational experiences (e.g., Dialogflow)
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Choosing the appropriate training and serving infrastructure
Use of edge compute
Leveraging pre-built ML models as a service
Measuring, monitoring, and troubleshooting machine learning models
Common sources of error (e.g., assumptions about data)
Operationalizing machine learning models
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Impact of dependencies of machine learning models
Continuous evaluation
Deploying an ML pipeline
Retraining of machine learning models (Cloud Machine Learning Engine, BigQuery ML, Kubeflow, Spark ML)

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The Google Professional-Data-Engineer Exam covers a wide range of topics, including data processing systems, data analysis, machine learning, and data security on Google Cloud Platform. Candidates are expected to have a thorough understanding of these topics and be able to apply them in real-world scenarios.

Google Certified Professional Data Engineer Exam Sample Questions (Q37-Q42):

NEW QUESTION # 37
You plan to deploy Cloud SQL using MySQL. You need to ensure high availability in the event of a zone failure. What should you do?

A. Create a Cloud SQL instance in one zone, and create a read replica in another zone within the same region.
B. Create a Cloud SQL instance in one zone, and create a failover replica in another zone within the same region.
C. Create a Cloud SQL instance in a region, and configure automatic backup to a Cloud Storage bucket in the same region.
D. Create a Cloud SQL instance in one zone, and configure an external read replica in a zone in a different region.

Answer: D

NEW QUESTION # 38
You are working on a sensitive project involving private user dat
a. 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 users' privacy?

A. Create a service account and allow the consultant to log on with it.
B. Grant the consultant the Viewer role on the project.
C. Create an anonymized sample of the data for the consultant to work with in a different project.
D. Grant the consultant the Cloud Dataflow Developer role on the project.

Answer: A

NEW QUESTION # 39
You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real- time analysis of aggregated trends. What should you do?

A. Use the MERGE statement to apply updates in batch every 60 seconds.
B. Use bq loadto load a batch of sensor data every 60 seconds.
C. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
D. Use the INSERT statement to insert a batch of data every 60 seconds.

Answer: D

Explanation:
Explanation

NEW QUESTION # 40
The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

A. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
B. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
C. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
D. Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

Answer: D

Explanation:
https://cloud.google.com/blog/products/gcp/performing-large-scale-mutations-in-bigquery

NEW QUESTION # 41
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

A. zone
B. node
C. label
D. type

Answer: A

Explanation:
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can also specify the number of workers, whether preemptible compute should be used, and the network settings.

NEW QUESTION # 42
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