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The Google Professional Machine Learning Engineer certification exam is intended for machine learning engineers, data scientists, and software engineers who are interested in designing and building scalable and efficient machine learning models on the Google Cloud Platform. Candidates who pass the certification exam will be able to demonstrate their proficiency in machine learning and will be recognized as a Google Professional Machine Learning Engineer.

Google Professional Machine Learning Engineer Sample Questions (Q132-Q137):

NEW QUESTION # 132
You are an ML engineer at a travel company. You have been researching customers' travel behavior for many years, and you have deployed models that predict customers' vacation patterns. You have observed that customers' vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

A. Store the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
B. Create versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
C. Store the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
D. Store the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

Answer: B

NEW QUESTION # 133
You are creating a deep neural network classification model using a dataset with categorical input values. Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

A. Convert each categorical value into an integer value.
B. Map the categorical variables into a vector of boolean values.
C. Convert the categorical string data to one-hot hash buckets.
D. Convert each categorical value into a run-length encoded string.

Answer: B

NEW QUESTION # 134
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?

A. Configure a Compute Engine instance with Spark and use Jupyter notebooks.
B. Set up a Vertex AI Workbench instance with a Spark kernel.
C. Set up a Dataproc cluster with Spark and use Jupyter notebooks.
D. Use Colab Enterprise with a Spark kernel.

Answer: C

Explanation:
Dataproc provides a managed Spark environment and integrates with Jupyter notebooks, ideal for large datasets and rapid prototyping. It reduces setup time compared to manual Spark configurations on Compute Engine or Vertex AI. Colab Enterprise is more suitable for small-scale prototyping rather than extensive Spark-based analysis.

NEW QUESTION # 135
You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100 000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training and are stored in a Cloud Storage bucket You need to be able to tune the model during each training run. How should you train the model?

A. Train a model for image classification by using Vertex Al AutoML.
B. Develop the model training code for image classification and train a model by using Vertex Al custom training.
C. Train a model for object detection by using Vertex Al AutoML.
D. Develop the model training code for object detection and tram a model by using Vertex Al custom training.

Answer: B

Explanation:
Image classification is a task where the model assigns a label to an image based on its content, such as "stop sign" or "speed limit"1. Object detection is a task where the model locates and identifies multiple objects in an image, and draws bounding boxes around them2. Since your dataset consists of images that were cropped to show one out of ten different traffic signs, you are dealing with an image classification problem, not an object detection problem. Therefore, you need to train a model for image classification, not object detection.
Vertex AI AutoML is a service that allows you to train and deploy high-quality ML models with minimal effort and machine learning expertise3. You can use Vertex AI AutoML to train a model for image classification by uploading your images and labels to a Vertex AI dataset, and then launching an AutoML training job4. However, Vertex AI AutoML does not allow you to tune the model during each training run, as it automatically selects the best model architecture and hyperparameters for your data4.
Vertex AI custom training is a service that allows you to train and deploy your own custom ML models using your own code and frameworks5. You can use Vertex AI custom training to train a model for image classification by writing your own model training code, such as using TensorFlow or PyTorch, and then creating and running a custom training job. Vertex AI custom training allows you to tune the model during each training run, as you can specify the model architecture and hyperparameters in your code, and use Vertex AI Hyperparameter Tuning to optimize them .
Therefore, the best option for your scenario is to develop the model training code for image classification and train a model by using Vertex AI custom training.
References:
* Image classification | TensorFlow Core
* Object detection | TensorFlow Core
* Introduction to Vertex AI AutoML | Google Cloud
* AutoML Vision | Google Cloud
* Introduction to Vertex AI custom training | Google Cloud
* [Custom training with TensorFlow | Vertex AI | Google Cloud]
* [Hyperparameter tuning overview | Vertex AI | Google Cloud]

NEW QUESTION # 136
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. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
C. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
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: C

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 # 137
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Updated Professional-Machine-Learning-Engineer Test Cram & Professional-Machine-Learning-Engineer Valid Dumps Ppt

Professional-Machine-Learning-Engineer Valid Dumps Ppt - Professional-Machine-Learning-Engineer Sample Questions Answers

Google Professional Machine Learning Engineer Sample Questions (Q132-Q137):

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