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Exam Professional Machine Learning Engineer topic 1 question 138 discussion

Actual exam question from Google's Professional Machine Learning Engineer
Question #: 138
Topic #: 1
[All Professional Machine Learning Engineer Questions]

You work for a company that manages a ticketing platform for a large chain of cinemas. Customers use a mobile app to search for movies they’re interested in and purchase tickets in the app. Ticket purchase requests are sent to Pub/Sub and are processed with a Dataflow streaming pipeline configured to conduct the following steps:
1. Check for availability of the movie tickets at the selected cinema.
2. Assign the ticket price and accept payment.
3. Reserve the tickets at the selected cinema.
4. Send successful purchases to your database.

Each step in this process has low latency requirements (less than 50 milliseconds). You have developed a logistic regression model with BigQuery ML that predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase, and this prediction should be added to the ticket purchase process. You want to identify the simplest way to deploy this model to production while adding minimal latency. What should you do?

  • A. Run batch inference with BigQuery ML every five minutes on each new set of tickets issued.
  • B. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline.
  • C. Export your model in TensorFlow format, deploy it on Vertex AI, and query the prediction endpoint from your streaming pipeline.
  • D. Convert your model with TensorFlow Lite (TFLite), and add it to the mobile app so that the promo code and the incoming request arrive together in Pub/Sub.
Show Suggested Answer Hide Answer
Suggested Answer: D 🗳️
by mil_spyro at Dec. 13, 2022, 6:43 p.m.

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behzadsw
Highly Voted 1 year, 10 months ago
Selected Answer: D
D as you want to do the prediction before the purchase
upvoted 11 times
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hiromi
Highly Voted 1 year, 10 months ago
Selected Answer: D
D - https://www.tensorflow.org/lite/guide
upvoted 5 times
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Amer95
Most Recent 2 months ago
Selected Answer: B
The incorrect answers introduce latency issues or operational inefficiencies: A: Running batch inference with BigQuery ML every five minutes causes delays due to interval-based processing. C: Deploying the model on Vertex AI introduces network latency from HTTP requests. D: Using TensorFlow Lite on mobile decentralizes inference but adds inconsistencies due to device variability and complicates updates. Correct Answer: B: Exporting the model in TensorFlow and integrating it into the Dataflow pipeline with tfx_bsl.public.beam.RunInference minimizes latency by keeping inference within the real-time streaming process. This ensures efficient and low-latency predictions.
upvoted 1 times
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NamitSehgal
2 months, 1 week ago
Selected Answer: A
Near Real-Time is Sufficient D. Convert to TFLite and deploy to the mobile app: This is impractical due to data availability, model updates, privacy concerns, and likely introduces more latency than a BigQuery ML batch prediction.
upvoted 1 times
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bobjr
4 months, 4 weeks ago
Selected Answer: C
D makes no senses -> if the prediction is made on the phone, why send it to the server ? C is the best choice because it splits the responsability, and use best practices and scalable tools
upvoted 2 times
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omermahgoub
6 months, 3 weeks ago
Selected Answer: B
B. Export your model in TensorFlow format, and add a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline. Here's why this approach offers minimal latency: In-Pipeline Prediction: The model is integrated directly into the Dataflow pipeline, enabling real-time predictions for each ticket purchase request without external calls. Dataflow Integration: tfx_bsl.public.beam.RunInference is a Beam utility specifically designed for integrating TensorFlow models into Dataflow pipelines, ensuring efficient execution.
upvoted 1 times
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Yan_X
7 months, 2 weeks ago
Selected Answer: B
B For D - How can we assume the model does be feasible to convert to Mobile app?
upvoted 1 times
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Krish6488
11 months, 3 weeks ago
Selected Answer: D
Question looks ambiguous! However considering some keywords like low latency and more importantly ML usage for maximising the ticket purchase requests using the promo code means that model embedded to the device looks more appropriate, however there are a lot of downsides to it like model management and upgrades but that does not seem to be the consideration here anyway. Just looking at low latency and ML to maximise the ticket sales, I will go with D as thats much simpler to implement
upvoted 2 times
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andresvelasco
1 year, 1 month ago
the whole question does not make too much sense to me. first of all, it seems that the Dataflow streaming job would "accept payment" meaning it communicates with payment gateways and back to the user, which does not sound right to do in Dataflow. the model "predicts whether offering a promo code for free popcorn increases the chance of a ticket purchase" is necesssarily executed before processing payment, so D seems the best. Awkward ....
upvoted 2 times
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M25
1 year, 5 months ago
Selected Answer: D
Went with D
upvoted 1 times
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TNT87
1 year, 6 months ago
Answer D
upvoted 4 times
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TNT87
1 year, 7 months ago
Selected Answer: B
is the simplest way to deploy the logistic regression model to production with minimal latency. Exporting the model in TensorFlow format and adding a tfx_bsl.public.beam.RunInference step to the existing Dataflow pipeline enables the model to be integrated directly into the ticket purchase process.
upvoted 1 times
tavva_prudhvi
1 year, 3 months ago
would also not be suitable because adding a tfx_bsl.public.beam.RunInference step to the Dataflow pipeline would still require the model to be executed within the same pipeline, potentially introducing additional latency and computational overhead.
upvoted 1 times
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TNT87
1 year, 7 months ago
Selected Answer: C
Option C is the best solution. Since the entire process has low latency requirements, running batch inference every five minutes is not a suitable option. Option B requires a TensorFlow model format, which may not be available since the model is created using BigQuery ML. Option D is not recommended because it requires deploying the model to the mobile app, which may not be feasible or desired. Deploying the model on Vertex AI and querying the prediction endpoint from the streaming pipeline adds minimal latency and is the simplest solution.
upvoted 1 times
TNT87
1 year, 7 months ago
Aiiii between B and C
upvoted 1 times
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TNT87
1 year, 7 months ago
Answer B
upvoted 1 times
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Scipione_
1 year, 8 months ago
Selected Answer: D
I perfectly agree with behzadsw. You send a Pub/Sub request when you already want to buy, you must add the coupon before this process.
upvoted 3 times
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John_Pongthorn
1 year, 8 months ago
Selected Answer: B
B (is it possible) along with What I get fromthis question. 1. this prediction should be added to the ticket purchase process ( it mean that it have to be include in rocessed with a Dataflow streaming pipeline 2.Each step in this process has low latency requirements (less than 50 milliseconds) , which signifies that whatever you will process in dataflow, there are no latency requirements issues
upvoted 3 times
John_Pongthorn
1 year, 8 months ago
https://www.tensorflow.org/tfx/tfx_bsl/api_docs/python/tfx_bsl/public/beam/RunInference
upvoted 1 times
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TNT87
1 year, 10 months ago
Answer D https://www.tensorflow. org/lite/guide
upvoted 1 times
TNT87
1 year, 7 months ago
Nope answer is B
upvoted 1 times
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mil_spyro
1 year, 10 months ago
Selected Answer: C
By deploying your model on Vertex AI, you can quickly and easily add the prediction step to your streaming pipeline, without the need to add additional infrastructure or manage model deployment and management.
upvoted 1 times
mymy9418
1 year, 10 months ago
but maybe D is faster?
upvoted 1 times
mil_spyro
1 year, 10 months ago
Hey I think you're right, predictions on the device itself, which avoids the need to send requests over the network. Should be D
upvoted 4 times
behzadsw
1 year, 10 months ago
You also want to do the prediction before the purchase right? so D is correct
upvoted 3 times
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C (25%)
B (20%)
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