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Google Professional Machine Learning Engineer Sample Questions (Q33-Q38):

NEW QUESTION # 33
You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

A. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
B. Build your custom containers to run distributed training jobs on Al Platform Training
C. Use a built-in model available on Al Platform Training
D. Build your custom container to run jobs on Al Platform Training

Answer: B

Explanation:
"ML framework and related dependencies are not supported by Al Platform Training" use custom containers "your model and your data are too large to fit in memory on a single machine " use distributed learning techniques

NEW QUESTION # 34
You created an ML pipeline with multiple input parameters. You want to investigate the tradeoffs between different parameter combinations. The parameter options are
* input dataset
* Max tree depth of the boosted tree regressor
* Optimizer learning rate
You need to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train and model complexity. You want your approach to be reproducible and track all pipeline runs on the same platform. What should you do?

A. 1 Create an experiment in Vertex Al Experiments
2. Create a Vertex Al pipeline with a custom model training job as part of the pipeline. Configure the pipelines parameters to include those you are investigating
3. Submit multiple runs to the same experiment using different values for the parameters
B. 1 Use BigQueryML to create a boosted tree regressor and use the hyperparameter tuning capability
2 Configure the hyperparameter syntax to select different input datasets. max tree depths, and optimizer teaming rates Choose the grid search option
C. 1 Create a Vertex Al pipeline with a custom model training job as part of the pipeline Configure the pipeline's parameters to include those you are investigating
2 In the custom training step, use the Bayesian optimization method with F1 score as the target to maximize
D. 1 Create a Vertex Al Workbench notebook for each of the different input datasets
2 In each notebook, run different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters
3 After each notebook finishes, append the results to a BigQuery table

Answer: A

Explanation:
The best option for investigating the tradeoffs between different parameter combinations is to create an experiment in Vertex AI Experiments, create a Vertex AI pipeline with a custom model training job as part of the pipeline, configure the pipeline's parameters to include those you are investigating, and submit multiple runs to the same experiment using different values for the parameters. This option allows you to leverage the power and flexibility of Google Cloud to compare the pipeline performance of the different parameter combinations measured in F1 score, time to train, and model complexity. Vertex AI Experiments is a service that can track and compare the results of multiple machine learning runs. Vertex AI Experiments can record the metrics, parameters, and artifacts of each run, and display them in a dashboard for easy visualization and analysis. Vertex AI Experiments can also help users optimize the hyperparameters of their models by using different search algorithms, such as grid search, random search, or Bayesian optimization1. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. A custom model training job is a type of pipeline step that can train a custom model by using a user-provided script or container. A custom model training job can accept pipeline parameters as inputs, which can be used to control the training logic or data source. By creating an experiment in Vertex AI Experiments, creating a Vertex AI pipeline with a custom model training job as part of the pipeline, configuring the pipeline's parameters to include those you are investigating, and submitting multiple runs to the same experiment using different values for the parameters, you can create a reproducible and trackable approach to investigate the tradeoffs between different parameter combinations.
The other options are not as good as option D, for the following reasons:
* Option A: Using BigQuery ML to create a boosted tree regressor and use the hyperparameter tuning capability, configuring the hyperparameter syntax to select different input datasets, max tree depths, and optimizer learning rates, and choosing the grid search option would not be able to handle different input datasets as a hyperparameter, and would not be as flexible and scalable as using Vertex AI Experiments and Vertex AI Pipelines. BigQuery ML is a service that can create and train machine learning models by using SQL queries on BigQuery. BigQuery ML can perform hyperparameter tuning by using the ML.FORECAST or ML.PREDICT functions, and specifying the hyperparameters option. BigQuery ML can also use different search algorithms, such as grid search, random search, or Bayesian optimization, to find the optimal hyperparameters. However, BigQuery ML can only tune the hyperparameters that are related to the model architecture or training process, such as max tree depth or learning rate. BigQuery ML cannot tune the hyperparameters that are related to the data source, such as input dataset. Moreover, BigQuery ML is not designed to work with Vertex AI Experiments or Vertex AI Pipelines, which can provide more features and flexibility for tracking and orchestrating machine learning workflows2.
* Option B: Creating a Vertex AI pipeline with a custom model training job as part of the pipeline, configuring the pipeline's parameters to include those you are investigating, and using the Bayesian optimization method with F1 score as the target to maximize in the custom training step would not be able to track and compare the results of multiple runs, and would require more skills and steps than using Vertex AI Experiments and Vertex AI Pipelines. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model.
A custom model training job is a type of pipeline step that can train a custom model by using a user-provided script or container. A custom model training job can accept pipeline parameters as inputs, which can be used to control the training logic or data source. However, using the Bayesian optimization method with F1 score as the target to maximize in the custom training step would require writing code, implementing the optimization algorithm, and defining the objective function. Moreover, this option would not be able to track and compare the results of multiple runs, as Vertex AI Pipelines does not have a built-in feature for recording and displaying the metrics, parameters, and artifacts of each run3.
* Option C: Creating a Vertex AI Workbench notebook for each of the different input datasets, running different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters, and appending the results to a BigQuery table would not be able to track and compare the results of multiple runs on the same platform, and would require more skills and steps than using Vertex AI Experiments and Vertex AI Pipelines. Vertex AI Workbench is a service that provides an integrated development environment for data science and machine learning. Vertex AI Workbench allows users to create and run Jupyter notebooks on Google Cloud, and access various tools and libraries for data analysis and machine learning. However, creating a Vertex AI Workbench notebook for each of the different input datasets, running different local training jobs with different combinations of the max tree depth and optimizer learning rate parameters, and appending the results to a BigQuery table would require creating multiple notebooks, writing code, setting up local environments, connecting to BigQuery, loading and preprocessing the data, training and evaluating the model, and writing the results to a BigQuery table. Moreover, this option would not be ableto track and compare the results of multiple runs on the same platform, as BigQuery is a separate service from Vertex AI Workbench, and does not have a dashboard for visualizing and analyzing the metrics, parameters, and artifacts of each run4.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 3: MLOps
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 1: Architecting low-code ML solutions, 1.1 Developing ML models by using BigQuery ML
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 3: Data Engineering for ML, Section 3.2: BigQuery for ML
* Vertex AI Experiments
* Vertex AI Pipelines
* BigQuery ML
* Vertex AI Workbench

NEW QUESTION # 35
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 loss
B. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value
C. An optimization objective that maximizes the Precision at a Recall value of 0.50
D. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

Answer: B

Explanation:
In this scenario, the goal is to create a custom fraud detection model using AutoML Tables. Fraud detection is a type of binary classification problem, where the model needs to predict whether a transaction is fraudulent or not. The optimization objective is a metric that defines how the model is trained and evaluated. AutoML Tables allows you to choose from different optimization objectives for binary classification problems, such as Log loss, Precision at a Recall value, AUC PR, and AUC ROC.
To choose the best optimization objective for fraud detection, we need to consider the characteristics of the problem and the data. Fraud detection is a problem where the positive class (fraudulent transactions) is very rare compared to the negative class (legitimate transactions). This means that the data is highly imbalanced, and the model needs to be sensitive to the minority class. Moreover, fraud detection is a problem where the cost of false negatives (missing a fraudulent transaction) is much higher than the cost of false positives (flagging a legitimate transaction as fraudulent). This means that the model needs to have high recall (the ability to detect all fraudulent transactions) while maintaining high precision (the ability to avoid false alarms).
Given these considerations, the best optimization objective for fraud detection is the one that maximizes the area under the precision-recall curve (AUC PR) value. The AUC PR value is a metric that measures the trade- off between precision and recall for different probability thresholds. A higher AUC PR value means that the model can achieve high precision and high recall at the same time. The AUC PR value is also more suitable for imbalanced data than the AUC ROC value, which measures the trade-off between the true positive rate and the false positive rate. The AUC ROC value can be misleading for imbalanced data, as it can give a high score even if the model has low recall or low precision.
Therefore, option C is the correct answer. Option A is not suitable, as Log loss is a metric that measures the difference between the predicted probabilities and the actual labels, and does not account for the trade-off between precision and recall. Option B is not suitable, as Precision at a Recall value is a metric that measures the precision at a fixed recall level, and does not account for the trade-off between precision and recall at different thresholds. Option D is not suitable, as AUC ROC is a metric that can be misleading for imbalanced data, as explained above.
References:
AutoML Tables documentation
Optimization objectives for binary classification
Precision-Recall Curves: How to Easily Evaluate Machine Learning Models in No Time ROC Curves and Area Under the Curve Explained (video)

NEW QUESTION # 36
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

A. Create a cluster on Dataproc for training
B. Use Al Platform for distributed training
C. Create a Managed Instance Group with autoscaling
D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

Answer: B

Explanation:
AI platform also contains kubeflow pipelines. you don't need to set up infrastructure to use it. For D you need to set up a kubernetes cluster engine. The question asks us to minimize infrastructure overheard.

NEW QUESTION # 37
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?

A. Drop the age feature from the dataset and train the model using the rest of the features.
B. Use k-means clustering to handle missing features
C. Drop all records from the dataset where age has been set to 0.
D. Replace the age field value for records with a value of 0 with the mean or median value from the dataset

Answer: C

Explanation:
Explanation

NEW QUESTION # 38
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Professional-Machine-Learning-Engineer Test Vce & Relevant Professional-Machine-Learning-Engineer Exam Dumps

Three User-Friendly and Easy-to-Install Pass4SureQuiz Professional-Machine-Learning-Engineer Exam Questions

Google Professional Machine Learning Engineer Sample Questions (Q33-Q38):