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Question 69/139
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?
Correct Answer: C
* Monitoring model performance is an essential part of production readiness, as it allows the team to detect and address any issues that may arise after deployment, such as data drift, model degradation, or errors.
* Other Options:
* A. Ensuring that training is reproducible is important for model development, but not necessarily for production readiness. Reproducibility helps the team to track and compare different experiments, but it does not guarantee that the model will perform well in production.
* B. Ensuring that all hyperparameters are tuned is also important for model development, but not sufficient for production readiness. Hyperparameter tuning helps the team to find the optimal configuration for the model, but it does not account for the dynamic and changing nature of the production environment.
* D. Ensuring that feature expectations are captured in the schema is a part of testing features and data, which the team has already done. The schema defines the expected format, type, and range of the features, and helps the team to validate and preprocess the data.
* Other Options:
* A. Ensuring that training is reproducible is important for model development, but not necessarily for production readiness. Reproducibility helps the team to track and compare different experiments, but it does not guarantee that the model will perform well in production.
* B. Ensuring that all hyperparameters are tuned is also important for model development, but not sufficient for production readiness. Hyperparameter tuning helps the team to find the optimal configuration for the model, but it does not account for the dynamic and changing nature of the production environment.
* D. Ensuring that feature expectations are captured in the schema is a part of testing features and data, which the team has already done. The schema defines the expected format, type, and range of the features, and helps the team to validate and preprocess the data.
- Question List (139q)
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- Question 18: You work on a team that builds state-of-the-art deep learnin...
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- Question 22: Your company manages an ecommerce website. You developed an ...
- Question 23: You have trained an XGBoost model that you plan to deploy on...
- Question 24: While running a model training pipeline on Vertex Al, you di...
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- Question 27: You are developing a custom image classification model in Py...
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- Question 31: You work at a gaming startup that has several terabytes of s...
- Question 32: You have successfully deployed to production a large and com...
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- Question 35: You have deployed a model on Vertex AI for real-time inferen...
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- Question 37: You are creating a model training pipeline to predict sentim...
- Question 38: You work at a gaming startup that has several terabytes of s...
- Question 39: You developed a Vertex Al ML pipeline that consists of prepr...
- Question 40: You work for a hospital that wants to optimize how it schedu...
- Question 41: You were asked to investigate failures of a production line ...
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- Question 44: During batch training of a neural network, you notice that t...
- Question 45: You are building a real-time prediction engine that streams ...
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- Question 49: You work at an ecommerce startup. You need to create a custo...
- Question 50: You work for a bank You have been asked to develop an ML mod...
- Question 51: You have developed an AutoML tabular classification model th...
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- Question 62: You trained a model on data stored in a Cloud Storage bucket...
- Question 63: You created an ML pipeline with multiple input parameters. Y...
- Question 64: You have recently used TensorFlow to train a classification ...
- Question 65: You recently trained a XGBoost model that you plan to deploy...
- Question 66: You work for a large retailer and you need to build a model ...
- Question 67: You work for a large technology company that wants to modern...
- Question 68: You have written unit tests for a Kubeflow Pipeline that req...
- Question 69: You recently joined a machine learning team that will soon r...
- Question 70: You recently trained an XGBoost model on tabular data You pl...
- Question 71: You have been asked to build a model using a dataset that is...
- Question 72: You recently developed a wide and deep model in TensorFlow. ...
- Question 73: You work for a telecommunications company You're building a ...
- Question 74: You recently deployed a model to a Vertex Al endpoint Your d...
- Question 75: You work for a company that sells corporate electronic produ...
- Question 76: You work at an organization that maintains a cloud-based com...
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- Question 78: You lead a data science team at a large international corpor...
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- Question 81: Your team needs to build a model that predicts whether image...
- Question 82: You are working on a binary classification ML algorithm that...
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- Question 91: You are building a linear regression model on BigQuery ML to...
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- Question 93: You recently used XGBoost to train a model in Python that wi...
- Question 94: You recently developed a deep learning model using Keras, an...
- Question 95: You are training a custom language model for your company us...
- Question 96: You created a model that uses BigQuery ML to perform linear ...
- Question 97: While performing exploratory data analysis on a dataset, you...
- Question 98: You are developing a model to help your company create more ...
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- Question 102: You have deployed a scikit-learn model to a Vertex Al endpoi...
- Question 103: You have built a model that is trained on data stored in Par...
- Question 104: Your organization's call center has asked you to develop a m...
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- Question 108: You work with a team of researchers to develop state-of-the-...
- Question 109: You are training a deep learning model for semantic image se...
- Question 110: You work on a growing team of more than 50 data scientists w...
- Question 111: You received a training-serving skew alert from a Vertex Al ...
- Question 112: You work for a semiconductor manufacturing company. You need...
- Question 113: You work for a retail company. You have a managed tabular da...
- Question 114: You recently designed and built a custom neural network that...
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- Question 121: You have created a Vertex Al pipeline that automates custom ...
- Question 122: Your data science team has requested a system that supports ...
- Question 123: You need to train a computer vision model that predicts the ...
- Question 124: You have recently created a proof-of-concept (POC) deep lear...
- Question 125: Your team is building a convolutional neural network (CNN)-b...
- Question 126: You developed a BigQuery ML linear regressor model by using ...
- Question 127: You have a demand forecasting pipeline in production that us...
- Question 128: You recently deployed a pipeline in Vertex Al Pipelines that...
- Question 129: You work at a leading healthcare firm developing state-of-th...
- Question 130: You developed a Python module by using Keras to train a regr...
- Question 131: You have developed a fraud detection model for a large finan...
- Question 132: You are developing a training pipeline for a new XGBoost cla...
- Question 133: As the lead ML Engineer for your company, you are responsibl...
- Question 134: You are going to train a DNN regression model with Keras API...
- Question 135: You are an ML engineer at an ecommerce company and have been...
- Question 136: You are responsible for building a unified analytics environ...
- Question 137: You deployed an ML model into production a year ago. Every m...
- Question 138: Your company stores a large number of audio files of phone c...
- Question 139: One of your models is trained using data provided by a third...
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