Question # 1
| You work for a magazine distributor and need to build a model that predicts which customers will renew their subscriptions for the upcoming year. Using your company’s historical data as your training set, you created a TensorFlow model and deployed it to AI Platform. You need to determine which customer attribute has the most predictive power for each prediction served by the model. What should you do? | | A. Use AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal. | | B. Stream prediction results to BigQuery. Use BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable. | | C. Use the AI Explanations feature on AI Platform. Submit each prediction request with the ‘explain’ keyword to retrieve feature attributions using the sampled Shapley method. | | D. Use the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded. Rank the feature importance in order of those that caused the most significant performance drop when removed from the model. |
C. Use the AI Explanations feature on AI Platform. Submit each prediction request with the ‘explain’ keyword to retrieve feature attributions using the sampled Shapley method.
Explanation:
Option A is incorrect because using AI Platform notebooks to perform a Lasso regression analysis on your model, which will eliminate features that do not provide a strong signal, is not a suitable way to determine which customer attribute has the most predictive power for each prediction served by the model. Lasso regression is a method of feature selection that applies a penalty to the coefficients of the linear model, and shrinks them to zero for irrelevant features1. However, this method assumes that the model is linear and additive, which may not be the case for a TensorFlow model. Moreover, this method does not provide feature attributions for each prediction, but rather for the entire dataset.
Option B is incorrect because streaming prediction results to BigQuery, and using BigQuery’s CORR(X1, X2) function to calculate the Pearson correlation coefficient between each feature and the target variable, is not a valid way to determine which customer attribute has the most predictive power for each prediction served by the model. The Pearson correlation coefficient is a measure of the linear relationship between two variables, ranging from -1 to 12. However, this method does not account for the interactions between features or the non-linearity of the model. Moreover, this method does not provide feature attributions for each prediction, but rather for the entire dataset.
Option C is correct because using the AI Explanations feature on AI Platform, and submitting each prediction request with the ‘explain’ keyword to retrieve feature attributions using the sampled Shapley method, is the best way to determine which customer attribute has the most predictive power for each prediction served by the model. AI Explanations is a service that allows you to get feature attributions for your deployed models on AI Platform3. Feature attributions are values that indicate how much each feature contributed to the prediction for a given instance4. The sampled Shapley method is a technique that uses the Shapley value, a game-theoretic concept, to measure the contribution of each feature to the prediction5. By using AI Explanations, you can get feature attributions for each prediction request, and identify the most important features for each customer.
Option D is incorrect because using the What-If tool in Google Cloud to determine how your model will perform when individual features are excluded, and ranking the feature importance in order of those that caused the most significant performance drop when removed from the model, is not a practical way to determine which customer attribute has the most predictive power for each prediction served by the model. The What-If tool is a tool that allows you to visualize and analyze your ML models and datasets. However, this method requires manually editing or removing features for each instance, and observing the change in the prediction. This method is not scalable or efficient, and may not capture the interactions between features or the non-linearity of the model.
References:
Lasso regression
Pearson correlation coefficient
AI Explanations overview
Feature attributions
Sampled Shapley method
[What-If tool overview]
Question # 2
| You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do? | | A. Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al. | | B. Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al. | | C. Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance. | | D. Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance. |
B. Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.
Explanation:
Customer churn is a binary classification problem, where the target variable is whether a customer has churned or not. Therefore, a logistic regression model is more suitable than a linear regression model, which is used for regression problems. A logistic regression model can output the probability of a customer churning, which can be used to rank the customers by their churn risk and take appropriate actions1.
BigQuery ML is a service that allows you to create and execute machine learning models in BigQuery using standard SQL queries2. You can use BigQuery ML to create a logistic regression model for customer churn prediction by using the CREATE MODEL statement and specifying the LOGISTIC_REG model type3. You can use the historical customer data as the input table for the model, and specify the features and the label columns3.
Vertex AI Model Registry is a central repository where you can manage the lifecycle of your ML models4. You can import models from various sources, such as BigQuery ML, AutoML, or custom models, and assign them to different versions and aliases4. You can also deploy models to endpoints, which are resources that provide a service URL for online prediction.
By registering the BigQuery ML model in Vertex AI Model Registry, you can leverage the Vertex AI features to evaluate and monitor the model performance4. You can use Vertex AI Experiments to track and compare the metrics of different model versions, such as accuracy, precision, recall, and AUC. You can also use Vertex AI Explainable AI to generate feature attributions that show how much each input feature contributed to the model’s prediction.
The other options are not suitable for your scenario, because they either use the wrong model type, such as linear regression, or they do not use Vertex AI to evaluate the model performance, which would limit the insights and actions you can take based on the model results.
References:
Logistic Regression for Machine Learning
Introduction to BigQuery ML | Google Cloud
Creating a logistic regression model | BigQuery ML | Google Cloud
Introduction to Vertex AI Model Registry | Google Cloud
[Deploy a model to an endpoint | Vertex AI | Google Cloud]
[Vertex AI Experiments | Google Cloud]
Question # 3
| You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage. You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline? | | A. Preprocess the input CSV file into a TFRecord file. | | B. Randomly select a 10 gigabyte subset of the data to train your model. | | C. Split into multiple CSV files and use a parallel interleave transformation. | | D. Set the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method. |
A. Preprocess the input CSV file into a TFRecord file.
Explanation:
According to the web search results, the TFRecord format is a recommended way to store large amounts of data efficiently and improve the performance of the data input pipeline123. The TFRecord format is a binary format that can be compressed and serialized, which reduces the I/O overhead and the memory footprint of the data1. The tf.data API provides tools to create and read TFRecord files easily1.
The other options are not as effective as option A. Option B would reduce the amount of data available for training and might affect the model accuracy. Option C would still require reading from a single CSV file at a time, which might not utilize the full bandwidth of the remote storage. Option D would only affect the order of the data elements, not the speed of reading them.
Question # 4
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?
| | A. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription. | | B. This is not a good result because the model is performing worse than predicting that people will always renew their subscription. | | C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group. | | D. This is a good result because the accuracy across both groups is greater than 80%. |
B. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
Explanation:
This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons:
It indicates that the model is not learning from the data, but rather memorizing the majority class. Since 90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the model’s accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription).
It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the model’s accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the model’s accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them.
References:
How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery
Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery
Question # 5
You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator;
estimator = tf.estimator.DNNRegressor(
feature_columns=[YOUR_LIST_OF_FEATURES],
hidden_units-[1024, 512, 256],
dropout=None)
Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?
| | A. Increase the dropout rate to 0.8 in_PREDICT mode by adjusting the TensorFlow Serving parameters | | B. Increase the dropout rate to 0.8 and retrain your model. | | C. Switch from CPU to GPU serving | | D. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16. |
D. Apply quantization to your SavedModel by reducing the floating point precision to tf.float16.
Explanation:
Quantization is a technique that reduces the numerical precision of the weights and activations of a neural network, which can improve the inference speed and reduce the memory footprint of the model1.
Reducing the floating point precision from tf.float64 to tf.float16 can potentially halve the latency and memory usage of the model, while having minimal impact on the accuracy2.
Increasing the dropout rate to 0.8 in either mode would not affect the latency, but would likely degrade the performance of the model significantly, as dropout is a regularization technique that randomly drops out units during training to prevent overfitting3.
Switching from CPU to GPU serving may or may not improve the latency, depending on the hardware specifications and the model complexity, but it would also incur additional costs and complexity for deployment4
Question # 6
| You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do? | | A. Use the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex Al services Deploy the workflow on Cloud Composer. | | B. Use the MLFlow SDK and deploy it on a Google Kubernetes Engine Cluster Create multiple components that use Dataflow and Vertex Al services. | | C. Use the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines. | | D. Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines. |
D. Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.
Explanation:
According to the web search results, TensorFlow Extended (TFX) is a platform for building end-to-end machine learning pipelines using TensorFlow1. TFX provides a set of components that can be orchestrated using either the TFX SDK or Kubeflow Pipelines. TFX components can handle different aspects of the pipeline, such as data ingestion, data validation, data transformation, model training, model evaluation, model serving, and more. TFX components can also leverage other Google Cloud services, such as Dataflow2 and Vertex AI3. Dataflow is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Vertex AI is a unified platform for machine learning development and deployment.
Vertex AI offers various services and tools for building, managing, and serving machine learning models. Therefore, option D is the best way to create a low maintenance, automated workflow for the given use case, as it allows you to use the TFX SDK to define and execute your pipeline components, and use Dataflow and Vertex AI services to scale and optimize your pipeline. The other options are not relevant or optimal for this scenario.
References:
TensorFlow Extended
Dataflow
Vertex AI
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions
Question # 7
| You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do? | | A. Create an object detection model that can localize the rust spots. | | B. Develop an image segmentation ML model to locate the boundaries of the rust spots. | | C. Develop a template matching algorithm using traditional computer vision libraries. | | D. Develop an image classification ML model to predict the presence of the disease. |
B. Develop an image segmentation ML model to locate the boundaries of the rust spots.
Explanation:
The best option for developing a solution that predicts the presence and severity of the disease with high accuracy is to develop an image segmentation ML model to locate the boundaries of the rust spots. Image segmentation is a technique that partitions an image into multiple regions, each corresponding to a different object or semantic category. Image segmentation can be used to detect and localize the rust spots in the images of crops, and measure their shape and size. This information can then be used to determine the presence and severity of the disease, as the rust spots are correlated to the disease symptoms. Image segmentation can also handle the variability of the rust spots, as it does not rely on predefined templates or thresholds. Image segmentation can be implemented using deep learning models, such as U-Net, Mask R-CNN, or DeepLab, which can learn from large-scale datasets and achieve high accuracy and robustness. The other options are not as suitable for developing a solution that predicts the presence and severity of the disease with high accuracy, because:
Creating an object detection model that can localize the rust spots would only provide the bounding boxes of the rust spots, not their exact boundaries. This would result in less precise measurements of the shape and size of the rust spots, and might affect the accuracy of the disease prediction. Object detection models are also more complex and computationally expensive than image segmentation models, as they have to perform both classification and localization tasks.
Developing a template matching algorithm using traditional computer vision libraries would require manually designing and selecting the templates for the rust spots, which might not capture the diversity and variability of the rust spots. Template matching algorithms are also sensitive to noise, occlusion, rotation, and scale changes, and might fail to detect the rust spots in different scenarios. Template matching algorithms are also less accurate and robust than deep learning models, as they do not learn from data.
Developing an image classification ML model to predict the presence of the disease would only provide a binary or categorical output, not the location or severity of the disease. Image classification models are also less informative and interpretable than image segmentation models, as they do not provide any spatial information or visual explanation for the prediction. Image classification models might also suffer from class imbalance or mislabeling issues, as the presence of the disease might not be consistent or clear across the images. References:
Image Segmentation | Computer Vision | Google Developers
Crop diseases and pests detection based on deep learning: a review | Plant Methods | Full Text
Using Deep Learning for Image-Based Plant Disease Detection
Computer Vision, IoT and Data Fusion for Crop Disease Detection Using …
On Using Artificial Intelligence and the Internet of Things for Crop …
Crop Disease Detection Using Machine Learning and Computer Vision
Google Professional-Machine-Learning-Engineer Exam Dumps
5 out of 5
Pass Your Google Professional Machine Learning Engineer Exam in First Attempt With Professional-Machine-Learning-Engineer Exam Dumps. Real Machine Learning Engineer Exam Questions As in Actual Exam!
— 285 Questions With Valid Answers
— Updation Date : 28-Mar-2025
— Free Professional-Machine-Learning-Engineer Updates for 90 Days
— 98% Google Professional Machine Learning Engineer Exam Passing Rate
PDF Only Price 49.99$
19.99$
Buy PDF
Speciality
Additional Information
Testimonials
Related Exams
- Number 1 Google Machine Learning Engineer study material online
- Regular Professional-Machine-Learning-Engineer dumps updates for free.
- Google Professional Machine Learning Engineer Practice exam questions with their answers and explaination.
- Our commitment to your success continues through your exam with 24/7 support.
- Free Professional-Machine-Learning-Engineer exam dumps updates for 90 days
- 97% more cost effective than traditional training
- Google Professional Machine Learning Engineer Practice test to boost your knowledge
- 100% correct Machine Learning Engineer questions answers compiled by senior IT professionals
Google Professional-Machine-Learning-Engineer Braindumps
Realbraindumps.com is providing Machine Learning Engineer Professional-Machine-Learning-Engineer braindumps which are accurate and of high-quality verified by the team of experts. The Google Professional-Machine-Learning-Engineer dumps are comprised of Google Professional Machine Learning Engineer questions answers available in printable PDF files and online practice test formats. Our best recommended and an economical package is Machine Learning Engineer PDF file + test engine discount package along with 3 months free updates of Professional-Machine-Learning-Engineer exam questions. We have compiled Machine Learning Engineer exam dumps question answers pdf file for you so that you can easily prepare for your exam. Our Google braindumps will help you in exam. Obtaining valuable professional Google Machine Learning Engineer certifications with Professional-Machine-Learning-Engineer exam questions answers will always be beneficial to IT professionals by enhancing their knowledge and boosting their career.
Yes, really its not as tougher as before. Websites like Realbraindumps.com are playing a significant role to make this possible in this competitive world to pass exams with help of Machine Learning Engineer Professional-Machine-Learning-Engineer dumps questions. We are here to encourage your ambition and helping you in all possible ways. Our excellent and incomparable Google Google Professional Machine Learning Engineer exam questions answers study material will help you to get through your certification Professional-Machine-Learning-Engineer exam braindumps in the first attempt.
Pass Exam With Google Machine Learning Engineer Dumps. We at Realbraindumps are committed to provide you Google Professional Machine Learning Engineer braindumps questions answers online. We recommend you to prepare from our study material and boost your knowledge. You can also get discount on our Google Professional-Machine-Learning-Engineer dumps. Just talk with our support representatives and ask for special discount on Machine Learning Engineer exam braindumps. We have latest Professional-Machine-Learning-Engineer exam dumps having all Google Google Professional Machine Learning Engineer dumps questions written to the highest standards of technical accuracy and can be instantly downloaded and accessed by the candidates when once purchased. Practicing Online Machine Learning Engineer Professional-Machine-Learning-Engineer braindumps will help you to get wholly prepared and familiar with the real exam condition. Free Machine Learning Engineer exam braindumps demos are available for your satisfaction before purchase order.
Send us mail if you want to check Google Professional-Machine-Learning-Engineer Google Professional Machine Learning Engineer DEMO before your purchase and our support team will send you in email.
If you don't find your dumps here then you can request what you need and we shall provide it to you.
Bulk Packages
$50
- Get 3 Exams PDF
- Get $33 Discount
- Mention Exam Codes in Payment Description.
Buy 3 Exams PDF
$70
- Get 5 Exams PDF
- Get $65 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF
$100
- Get 5 Exams PDF + Test Engine
- Get $105 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF + Engine
 Jessica Doe
Machine Learning Engineer
We are providing Google Professional-Machine-Learning-Engineer Braindumps with practice exam question answers. These will help you to prepare your Google Professional Machine Learning Engineer exam. Buy Machine Learning Engineer Professional-Machine-Learning-Engineer dumps and boost your knowledge.
|
