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Quiz Google - Professional-Machine-Learning-Engineer - Google Professional Machine Learning Engineer–Valid Exam Questions Answers

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Google Professional Machine Learning Engineer Sample Questions (Q165-Q170):

NEW QUESTION # 165
You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products. Which approach should you use to build the model?

• A. Convolutional Neural Networks (CNN)
• B. Reinforcement learning
• C. Recommender system
• D. Recurrent Neural Networks (RNN)

Answer: A

Explanation:
Option A is incorrect because reinforcement learning is not a suitable approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. Reinforcement learning is a type of machine learning that learns from its own actions and rewards, rather than from labeled data or explicit feedback1. Reinforcement learning is more suitable for problems that involve sequential decision making, such as games, robotics, or control systems1. However, defect detection is a problem that involves image classification or segmentation, which requires supervised learning, not reinforcement learning.
Option B is incorrect because a recommender system is not a relevant approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. A recommender system is a system that suggests items or actions to users based on their preferences, behavior, or context2. A recommender system is more suitable for problems that involve personalization, such as e-commerce, entertainment, or social media2. However, defect detection is a problem that involves image classification or segmentation, which requires supervised learning, not recommender system.
Option C is incorrect because recurrent neural networks (RNN) are not the most efficient approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. RNNs are a type of neural networks that can process sequential data, such as text, speech, or video, by maintaining a hidden state that captures the temporal dependencies3. RNNs are more suitable for problems that involve natural language processing, speech recognition, or video analysis3. However, defect detection is a problem that involves image classification or segmentation, which does not require temporal dependencies, but rather spatial dependencies. Moreover, RNNs are computationally expensive and prone to vanishing or exploding gradients4.
Option D is correct because convolutional neural networks (CNN) are the best approach to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. CNNs are a type of neural networks that can process image data, by applying convolutional filters that extract local features and reduce the dimensionality of the data5. CNNs are more suitable for problems that involve image classification, object detection, or segmentation5. CNNs can preprocess the images with lower computation to quickly extract features of defects in products, by using techniques such as pooling, dropout, or batch normalization6.
Reference:
Reinforcement learning
Recommender system
Recurrent neural network
Vanishing and exploding gradients
Convolutional neural network
CNN techniques
[Defect detection]
[Image classification]
[Image segmentation]

 

NEW QUESTION # 166
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?

• A. The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is
  13.12%
• B. The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is
  20.85%
• C. The true class frequency for Romance is 0.78 and the predicted class frequency for Adventure is (0.47-
  0.32)
• D. The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is
  20.85%*0.32

Answer: A

 

NEW QUESTION # 167
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?

• A. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
• B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
• C. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
• D. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.

Answer: B

Explanation:
Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives1 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path2 To automate the execution of unit tests for a Kubeflow Pipeline that require custom libraries, you can use Cloud Build to set an automated trigger to execute the unit tests when changes are pushed to your development branch in Cloud Source Repositories. You can specify the steps of the build in a YAML or JSON file, such as installing the custom libraries, running the unit tests, and reporting the results. You can also use Cloud Build to build and deploy the Kubeflow Pipeline components if the unit tests pass3 The other options are not recommended or feasible. Writing a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run is not a good practice, as it does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Setting up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories and using a Pub
/Sub trigger for Cloud Run or Cloud Function to execute the unit tests is unnecessarily complex and inefficient, as it adds extra steps and latency to the process. Cloud Run and Cloud Function are also not designed for executing unit tests, as they have limitations on the memory, CPU, and execution time45 References: 1: Cloud Build overview 2: Creating and managing build triggers 3: Building and deploying Kubeflow Pipelines using Cloud Build 4: Cloud Run documentation 5: Cloud Functions documentation

 

NEW QUESTION # 168
You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

• A. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
• B. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.
• C. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
• D. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

Answer: A

 

NEW QUESTION # 169
You want to migrate a scikrt-learn classifier model to TensorFlow. You plan to train the TensorFlow classifier model using the same training set that was used to train the scikit-learn model and then compare the performances using a common test set. You want to use the Vertex Al Python SDK to manually log the evaluation metrics of each model and compare them based on their F1 scores and confusion matrices. How should you log the metrics?

• A. Option B
• B. Option A
• C. Option D
• D. Option C

Answer: C

Explanation:
To log the metrics of a machine learning model in TensorFlow using the Vertex AI Python SDK, you should utilize the aiplatform.log_metrics function to log the F1 score and aiplatform.log_classification_metrics function to log the confusion matrix. These functions allow users to manually record and store evaluation metrics for each model, facilitating an efficient comparison based on specific performance indicators like F1 scores and confusion matrices. Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI and TensorFlow.
Vertex AI Python SDK reference | Google Cloud
Logging custom metrics | Vertex AI
Migrating from scikit-learn to TensorFlow | TensorFlow

 

NEW QUESTION # 170
......

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