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Exam Professional Machine Learning Engineer All Questions

View all questions & answers for the Professional Machine Learning Engineer exam

Exam Professional Machine Learning Engineer topic 1 question 59 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 59
Topic #: 1
[All Professional Machine Learning Engineer Questions]

Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

  • A. Use Kubeflow Pipelines to execute the experiments. Export the metrics file, and query the results using the Kubeflow Pipelines API.
  • B. Use AI Platform Training to execute the experiments. Write the accuracy metrics to BigQuery, and query the results using the BigQuery API.
  • C. Use AI Platform Training to execute the experiments. Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
  • D. Use AI Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API.
Show Suggested Answer Hide Answer
Suggested Answer: A 🗳️

Comments

Chosen Answer:
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Dunnoth
Highly Voted 1 year, 9 months ago
Selected Answer: A
Old answer is A. New answer (not available) would be Virtex AI experiments which comes with monitoring API inbuilt. https://cloud.google.com/blog/topics/developers-practitioners/track-compare-manage-experiments-vertex-ai-experiments
upvoted 15 times
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Celia20210714
Highly Voted 3 years, 4 months ago
ANS: A https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.
upvoted 12 times
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eico
Most Recent 3 months ago
Selected Answer: A
This is an old question, when Vertex AI didn't have Vertex AI Experiments. The old answer is A
upvoted 1 times
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San1111111111
4 months, 1 week ago
Shoudlnt it be B? VAI has inbuilt VAI experiments and metadata to track metrics..
upvoted 1 times
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dija123
5 months, 1 week ago
Selected Answer: A
Should agree with A
upvoted 1 times
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PhilipKoku
5 months, 3 weeks ago
Selected Answer: A
A) Kubeflow pipelines
upvoted 1 times
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Mickey321
1 year ago
Selected Answer: C
either A or C but going with C due to minimal effort
upvoted 5 times
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Liting
1 year, 4 months ago
Selected Answer: A
I agree with tavva_prudhvi that cloud monitoring is not the best option to do machine learning tracking, Metadata is a better option for that purpose
upvoted 1 times
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tavva_prudhvi
1 year, 4 months ago
Selected Answer: A
Option C suggests using AI Platform Training to execute the experiments and write the accuracy metrics to Cloud Monitoring. While Cloud Monitoring can be used to monitor and collect metrics from various services in Google Cloud, it is not specifically designed for machine learning experiments tracking. Using Cloud Monitoring for tracking machine learning experiments may not provide the same level of functionality and flexibility as Kubeflow Pipelines or AI Platform Training. Additionally, querying the results from Cloud Monitoring may not be as straightforward as using the APIs provided by Kubeflow Pipelines or AI Platform Training. Therefore, while Cloud Monitoring can be used as a general-purpose monitoring solution, it may not be the best option for tracking and reporting machine learning experiments.
upvoted 2 times
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PST21
1 year, 5 months ago
Cloud monitoring may not be the most suitable option for tracking and reporting experiments, only because of this option C is out & I stick to A
upvoted 1 times
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M25
1 year, 6 months ago
Selected Answer: A
Went with A
upvoted 2 times
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lucaluca1982
1 year, 7 months ago
Selected Answer: B
It is B
upvoted 1 times
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John_Pongthorn
1 year, 9 months ago
This is the question, Try out and choose what is the closet to this lab.Last updated Jan 21, 2023 https://codelabs.developers.google.com/vertex_experiments_pipelines_intro#0
upvoted 1 times
John_Pongthorn
1 year, 9 months ago
As The lab walk me through how to create pipe line to experiment , it use Kubeflow and apply experiment SDK
upvoted 1 times
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ares81
1 year, 10 months ago
Selected Answer: C
Vertex AI Experiments + Cloud Monitoring for the metrics. It's C!
upvoted 3 times
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mymy9418
1 year, 11 months ago
Selected Answer: C
I like C https://cloud.google.com/monitoring/mql
upvoted 1 times
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Pancy
1 year, 11 months ago
C: Google has already provided inhouse monitoring mechanism so no need to query or use any other tool. https://cloud.google.com/bigquery/docs/monitoring
upvoted 1 times
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Mohamed_Mossad
2 years, 5 months ago
https://www.kubeflow.org/docs/components/pipelines/introduction/#what-is-kubeflow-pipelines
upvoted 1 times
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Community vote distribution
A (35%)
C (25%)
B (20%)
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