Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer Exam Practice Test
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, Scikit-learn, and custom libraries. What should you do?
Correct Answer: C
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You were asked to investigate failures of a production line component based on sensor readings.
After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents.
You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents.
You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?
Correct Answer: A
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You work for a manufacturing company that owns a high-value machine which has several machine settings and multiple sensors. A history of the machine's hourly sensor readings and known failure event data are stored in BigQuery. You need to predict if the machine will fail within the next 3 days in order to schedule maintenance before the machine fails. Which data preparation and model training steps should you take?
Correct Answer: A
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You recently developed a deep learning model. To test your new model, you trained it for a few epochs on a large dataset. You observe that the training and validation losses barely changed during the training run. You want to quickly debug your model. What should you do first?
Correct Answer: D
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You are training and deploying updated versions of a regression model with tabular data by using Vertex AI Pipelines, Vertex AI Training, Vertex AI Experiments, and Vertex AI Endpoints. The model is deployed in a Vertex AI endpoint, and your users call the model by using the Vertex AI endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model. What should you do?
Correct Answer: D
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You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?
Correct Answer: D
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You work with a team of researchers to develop state-of-the-art algorithms for financial analysis.
Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?
Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?
Correct Answer: D
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You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?
Correct Answer: C
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You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed. You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex AI Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?
Correct Answer: A
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While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?
Correct Answer: A
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You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?
Correct Answer: A
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You recently deployed an image classification model on Google Cloud. You used Cloud Build to build a CI/CD pipeline for the model. You need to ensure that the model stays up-to-date with data and code changes by using an efficient retraining process. What should you do?
Correct Answer: C
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You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?
Correct Answer: D
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You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but the stakeholders are concerned about the model's potential for bias based on customer demographics. You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?
Correct Answer: D
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You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters.
You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate.
You configure the following settings:
- For the embedding dimension, you set the type to INTEGER with a
minValue of 16 and maxValue of 64.
- For the learning rate, you set the type to DOUBLE with a minValue of
10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate.
You configure the following settings:
- For the embedding dimension, you set the type to INTEGER with a
minValue of 16 and maxValue of 64.
- For the learning rate, you set the type to DOUBLE with a minValue of
10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
Correct Answer: A
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