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The Google Professional-Machine-Learning-Engineer exam is designed to test the candidate's skills in various areas such as data preparation, feature engineering, model development, training, and deployment. Professional-Machine-Learning-Engineer exam also evaluates the candidate's ability to optimize and tune machine learning models for improved performance and scalability.

Google Professional Machine Learning Engineer exam is an advanced-level certification offered by Google Cloud. Google Professional Machine Learning Engineer certification is aimed at individuals who have extensive experience in machine learning and are seeking to become experts in this field. Professional-Machine-Learning-Engineer Exam is designed to test the candidate's proficiency in designing, building, and deploying machine learning models using Google Cloud technologies.

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Google Professional Machine Learning Engineer Sample Questions (Q142-Q147):

NEW QUESTION # 142
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

• A. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
• B. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
• C. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
• D. Create a tf.data.Dataset.prefetch transformation

Answer: B

Explanation:
Cite from Google Pag: to construct a Dataset from data in memory, use tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset(). tf.data.Dataset is for data in memory. tf.data.TFRecordDataset is for data in non-memory storage.
https://cloud.google.com/architecture/ml-on-gcp-best-practices#store-image-video-audio-and-unstructured-data-on-cloud-storage
" Store image, video, audio and unstructured data on Cloud Storage Store these data in large container formats on Cloud Storage. This applies to sharded TFRecord files if you're using TensorFlow, or Avro files if you're using any other framework. Combine many individual images, videos, or audio clips into large files, as this will improve your read and write throughput to Cloud Storage. Aim for files of at least 100mb, and between 100 and 10,000 shards. To enable data management, use Cloud Storage buckets and directories to group the shards. "

NEW QUESTION # 143
Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

• A. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.
• B. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.
• C. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.
• D. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.

Answer: D

Explanation:
A Dataflow streaming pipeline is a cost-effective way to process large volumes of real-time data from sensors. The RunInference API is a Dataflow transform that allows you to run online predictions on your streaming data using your ML models. By using the RunInference API, you can avoid the latency and cost of using a separate prediction service. The automatic model refresh feature enables you to update your models in the pipeline without redeploying the pipeline. This way, you can ensure that your models are always up-to- date and accurate. By deploying a Dataflow streaming pipeline with the RunInference API and using automatic model refresh, you can achieve sub-millisecond predictions, 24/7 availability, and low operational overhead for your ML models. References:
* Dataflow documentation
* RunInference API documentation
* Automatic model refresh documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 144
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

• A. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
• B. Train a model using AutoML Vision and use the "export for Coral" option.
• C. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.
• D. Train a model using AutoML Vision and use the "export for Core ML" option.

Answer: D

Explanation:
AutoML Vision is a Google Cloud service that allows you to train custom ML models for image classification, object detection, and segmentation without writing any code. You can use AutoML Visionto upload your training data, label it, and train a model using a graphical user interface. You can also evaluate the model's performance and export it for deployment. One of the export options is Core ML, which is a framework that lets you integrate ML models into iOS applications. Core ML optimizes the model for on-device performance, power efficiency, and minimal memory footprint. By using AutoML Vision and Core ML, you can minimize code development and have a model that is optimized for inference on mobile phones.
References:
* AutoML Vision documentation
* Core ML documentation

NEW QUESTION # 145
You are developing an ML model in a Vertex Al Workbench notebook. You want to track artifacts and compare models during experimentation using different approaches. You need to rapidly and easily transition successful experiments to production as you iterate on your model implementation. What should you do?

• A. 1 Create a Vertex Al pipeline Use the Dataset and Model artifact types from the Kubeflow Pipelines. DSL as the inputs and outputs of the components in your pipeline.
  2. In your training component use the Vertex Al SDK to create an experiment run Configure the log_params and log_metrics functions to track parameters and metrics of your experiment.
• B. 1. Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, save your dataset to a Cloud Storage bucket and upload the models to Vertex Al Model Registry.
  2 After a successful experiment create a Vertex Al pipeline.
• C. 1 Initialize the Vertex SDK with the name of your experiment Log parameters and metrics for each experiment, and attach dataset and model artifacts as inputs and outputs to each execution.
  2 After a successful experiment create a Vertex Al pipeline.
• D. 1 Create a Vertex Al pipeline with parameters you want to track as arguments to your Pipeline Job Use the Metrics. Model, and Dataset artifact types from the Kubeflow Pipelines DSL as the inputs and outputs of the components in your pipeline.
  2. Associate the pipeline with your experiment when you submit the job.

Answer: C

Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Workbench is an integrated development environment (IDE) that allows you to create and run Jupyter notebooks on Google Cloud. You can use Vertex AI Workbench to develop your ML model in Python, using libraries such as TensorFlow, PyTorch, scikit-learn, etc. You can also use the Vertex SDK, which is a Python client library for Vertex AI, to track artifacts and compare models during experimentation. You can use the aiplatform.init function to initialize the Vertex SDK with the name of your experiment. You can use the aiplatform.start_run and aiplatform.end_run functions to create and close an experiment run. You can use the aiplatform.log_params and aiplatform.log_metrics functions to log the parameters and metrics for each experiment run. You can also use the aiplatform.log_datasets and aiplatform.log_model functions to attach the dataset and model artifacts as inputs and outputs to each experiment run. These functions allow you to record and store the metadata and artifacts of your experiments, and compare them using the Vertex AI Experiments UI. After a successful experiment, you can create a Vertex AI pipeline, which is a way to automate and orchestrate your ML workflows. You can use the aiplatform.PipelineJob class to create a pipeline job, and specify the components and dependencies of your pipeline. You can also use the aiplatform.CustomContainerTrainingJob class to create a custom container training job, and use the run method to run the job as a pipeline component. You can use the aiplatform.Model.deploy method to deploy your model as a pipeline component. You can also use the aiplatform.Model.monitor method to monitor your model as a pipeline component. By creating a Vertex AI pipeline, you can rapidly and easily transition successful experiments to production, and reuse and share your ML workflows. This solution requires minimal changes to your code, and leverages the Vertex AI services and tools to streamline your ML development process. Reference: The answer can be verified from official Google Cloud documentation and resources related to Vertex AI, Vertex AI Workbench, Vertex SDK, and Vertex AI pipelines.
Vertex AI | Google Cloud
Vertex AI Workbench | Google Cloud
Vertex SDK for Python | Google Cloud
Vertex AI pipelines | Google Cloud

NEW QUESTION # 146
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

• A. Use the class distribution to generate 10% positive examples
• B. Remove negative examples until the numbers of positive and negative examples are equal
• C. Use a convolutional neural network with max pooling and softmax activation
• D. Downsample the data with upweighting to create a sample with 10% positive examples

Answer: D

Explanation:
The class imbalance problem is a common challenge in machine learning, especially in classification tasks. It occurs when the distribution of the target classes is highly skewed, such that one class (the majority class) has much more examples than the other class (the minority class). The minority class is often the more interesting or important class, such as failure incidents, fraud cases, or rare diseases. However, most machine learning algorithms are designed to optimize the overall accuracy, which can be biased towards the majority class and ignore the minority class. This can result in poor predictive performance, especially for the minority class.
There are different techniques to deal with the class imbalance problem, such as data-level methods, algorithm-level methods, and evaluation-level methods1. Data-level methods involve resampling the original dataset to create a more balanced class distribution. There are two main types of data-level methods:
oversampling and undersampling. Oversampling methods increase the number of examples in the minority class, either by duplicating existing examples or by generating synthetic examples. Undersampling methods reduce the number of examples in the majority class, either by randomly removing examples or by using clustering or other criteria to select representative examples. Both oversampling and undersampling methods can be combined with upweighting or downweighting, which assign different weights to the examples according to their class frequency, to further balance the dataset.
For the use case of investigating failures of a production line component based on sensor readings, the best option is to downsample the data with upweighting to create a sample with 10% positive examples. This option involves randomly removing some of the negative examples (the majority class) until the ratio of positive to negative examples is 1:9, and then assigning higher weights to the positive examples to compensate for their low frequency. This option can create a more balanced dataset that can improve the performance of the classification models, while preserving the diversity and representativeness of the original data. This option can also reduce the computation time and memory usage, as the size of the dataset is reduced. Therefore, downsampling the data with upweighting to create a sample with 10% positive examples is the best option for this use case.
References:
* A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks

NEW QUESTION # 147
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