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Google Professional Machine Learning Engineer Sample Questions (Q26-Q31):

NEW QUESTION # 26
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

A. * Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.B. * Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online predictionC. * Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queueD. * Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

Answer: C

Explanation:
https://cloud.google.com/architecture/data-preprocessing-for-ml-with-tf-transform-pt1#where_to_do_preprocessing

NEW QUESTION # 27
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

A. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).B. Create a tf.data.Dataset.prefetch transformationC. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().D. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training

Answer: D

NEW QUESTION # 28
You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

A. An optimization objective that minimizes Log lossB. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) valueC. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) valueD. An optimization objective that maximizes the Precision at a Recall value of 0.50

Answer: C

Explanation:
https://stats.stackexchange.com/questions/262616/roc-vs-precision-recall-curves-on-imbalanced-dataset
https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc
https://icaiit.org/proceedings/6th_ICAIIT/1_3Fayzrakhmanov.pdf The problem of fraudulent transactions detection, which is an imbalanced classification problem (most transactions are not fraudulent), you want to maximize both precision and recall; so the area under the PR curve. As a matter of fact, the question asks you to focus on detecting fraudulent transactions (maximize true positive rate, a.k.a. Recall) while minimizing false positives (a.k.a. maximizing Precision). Another way to see it is this: for imbalanced problems like this one you'll get a lot of true negatives even from a bad model (it's easy to guess a transaction as "non-fraudulent" because most of them are!), and with high TN the ROC curve goes high fast, which would be misleading. So you wanna avoid dealing with true negatives in your evaluation, which is precisely what the PR curve allows you to do.

NEW QUESTION # 29
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

A. Change the search algorithm from Bayesian search to random search.B. Decrease the range of floating-point valuesC. Set the early stopping parameter to TRUED. Decrease the maximum number of trials during subsequent training phases.E. Decrease the number of parallel trials

Answer: C,D

Explanation:
Reference:
https://cloud.google.com/ai-platform/training/docs/using-hyperparameter-tuning#early-stopping

NEW QUESTION # 30
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

A. Normalize the data for the training, and test datasets as two separate steps.B. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.C. Add more data to your test set to ensure that you have a fair distribution and sample for testingD. Split the training and test data based on time rather than a random split to avoid leakage

Answer: C

NEW QUESTION # 31
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