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Achieving the Google Professional Machine Learning Engineer Certification is a significant accomplishment for professionals in the field of machine learning. It demonstrates a high level of expertise in designing, implementing, and deploying machine learning models using Google Cloud Platform. Google Professional Machine Learning Engineer certification also provides opportunities for career advancement and recognition as a leader in the field of machine learning.

Google Professional Machine Learning Engineer Sample Questions (Q50-Q55):

NEW QUESTION # 50
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework.
Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs.
You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

A. Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.
B. Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.
C. Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
D. Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization.

Answer: D

Explanation:
Vertex AI Experiments is a service that allows you to track, compare, and optimize your ML experiments on Google Cloud. You can use Vertex AI Experiments to log metrics and parameters from your TensorFlow models, and then visualize them in Vertex AI TensorBoard. Vertex AI TensorBoard is a managed service that provides a web interface for viewing and debugging your ML experiments. You can use Vertex AI TensorBoard to compare different runs, inspect model graphs, analyze scalars, histograms, images, and more.
By using Vertex AI Experiments and Vertex AI TensorBoard, you can simplify your ML experiment tracking and visualization workflow, and avoid the overhead of setting up and maintaining your own Cloud Functions, Cloud Storage buckets, or VMs. References:
* [Vertex AI Experiments documentation]
* [Vertex AI TensorBoard documentation]
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 51
You are collaborating on a model prototype with your team. You need to create a Vertex Al Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

A. 1. Grant the Vertex Al User role to the default Compute Engine service account.
2. Grant the Service Account User role to each team member on the default Compute Engine service account.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the default Compute Engine service account.
B. 1 Create a new service account and grant it the Vertex Al User role.
2 Grant the Service Account User role to each team member on the service account.
3. Grant the Notebook Viewer role to each team member.
4 Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.
C. 1 Grant the Vertex Al User role to the primary team member.
2. Grant the Notebook Viewer role to the other team members.
3. Provision a Vertex Al Workbench user-managed notebook instance that uses the primary user's account.
D. 1. Create a new service account and grant it the Notebook Viewer role.
2 Grant the Service Account User role to each team member on the service account.
3 Grant the Vertex Al User role to each team member.
4. Provision a Vertex Al Workbench user-managed notebook instance that uses the new service account.

Answer: B

Explanation:
To create a Vertex AI Workbench environment for your team and limit access to other employees in your project, you should follow these steps:
* Create a new service account and grant it the Vertex AI User role. This role grants full access to all resources in Vertex AI, including creating and managing notebook instances1.
* Grant the Service Account User role to each team member on the service account. This role allows the team members to impersonate the service account and use its permissions2.
* Grant the Notebook Viewer role to each team member. This role allows the team members to view and
* connect to the notebook instance, but not to modify or delete it3.
* Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account.
This way, the notebook instance will run as the service account and only the team members who have the Service Account User and Notebook Viewer roles will be able to access it.
References:
* 1: Vertex AI access control with IAM | Google Cloud
* 2: Understanding service accounts | Cloud IAM Documentation
* 3: Manage access to a Vertex AI Workbench instance | Google Cloud
* [4]: Create and manage Vertex AI Workbench instances | Google Cloud

NEW QUESTION # 52
You are building a TensorFlow model for a financial institution that predicts the impact of consumer spending on inflation globally. Due to the size and nature of the data, your model is long-running across all types of hardware, and you have built frequent checkpointing into the training process. Your organization has asked you to minimize cost. What hardware should you choose?

A. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a non-preemptible v3-8 TPU
B. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs
C. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a preemptible v3-8 TPU
D. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU

Answer: C

Explanation:
The best hardware to choose for your model while minimizing cost is a Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a preemptible v3-8 TPU. This hardware configuration can provide you with high performance, scalability, and efficiency for your TensorFlow model, as well as low cost and flexibility for your long-running and checkpointing process. The v3-8 TPU is a cloud tensor processing unit (TPU) device, which is a custom ASIC chip designed by Google to accelerate ML workloads.
It can handle large and complex models and datasets, and offer fast and stable training and inference. The n1-standard-16 is a general-purpose VM that can support the CPU and memory requirements of your model, as well as the data preprocessing and postprocessing tasks. By choosing a preemptible v3-8 TPU, you can take advantage of the lower price and availability of the TPU devices, as long as you can tolerate the possibility of the device being reclaimed by Google at any time. However, since you have built frequent checkpointing into your training process, you can resume your model from the last saved state, and avoid losing any progress or data. Moreover, you can use the Vertex AI Workbench user-managed notebooks to create and manage your notebooks instances, and leverage the integration with Vertex AI and other Google Cloud services.
The other options are not optimal for the following reasons:
* A. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs is not a good option, as it has higher cost and lower performance than the v3-8 TPU. The NVIDIA P100 GPUs are the previous generation of GPUs from NVIDIA, which have lower performance, scalability, and efficiency than the latest NVIDIA A100 GPUs or the TPUs. They also have higher price and lower availability than the preemptible TPUs, which can increase the cost and complexity of your solution.
* B. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU is not a good option, as it has higher cost and lower performance than the v3-8 TPU. It also has less GPU memory and compute power than the option with 4 NVIDIA P100 GPUs, which can limit the size and complexity of your model, and affect the training and inference speed and quality.
* C. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with a
* non-preemptible v3-8 TPU is not a good option, as it has higher cost and lower flexibility than the preemptible v3-8 TPU. The non-preemptible v3-8 TPU has the same performance, scalability, and efficiency as the preemptible v3-8 TPU, but it has higher price and lower availability, as it is reserved for your exclusive use. Moreover, since your model is long-running and checkpointing, you do not need the guarantee of the device not being reclaimed by Google, and you can benefit from the lower cost and higher availability of the preemptible v3-8 TPU.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* Cloud TPU
* Vertex AI Workbench user-managed notebooks
* Preemptible VMs
* NVIDIA Tesla P100 GPU

NEW QUESTION # 53
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 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.
B. 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.
C. 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.
D. 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.

Answer: A

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.
References: 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 # 54
You are creating a social media app where pet owners can post images of their pets. You have one million user uploaded images with hashtags. You want to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images.
What should you do?

A. Download a pretrained convolutional neural network, and use the model to generate embeddings of the input images. Measure similarity between embeddings to make recommendations.
B. Retrieve image labels and dominant colors from the input images using the Vision API. Use these properties and the hashtags to make recommendations.
C. Use the provided hashtags to create a collaborative filtering algorithm to make recommendations.
D. Download a pretrained convolutional neural network, and fine-tune the model to predict hashtags based on the input images. Use the predicted hashtags to make recommendations.

Answer: A

Explanation:
The best option to build a comprehensive system that recommends images to users that are similar in appearance to their own uploaded images is to download a pretrained convolutional neural network (CNN), and use the model to generate embeddings of the input images. Embeddings are low-dimensional representations of high-dimensional data that capture the essential features and semantics of the data. By using a pretrained CNN, you can leverage the knowledge learned from large-scale image datasets, such as ImageNet, and apply it to your own domain. A pretrained CNN can be used as a feature extractor, where the output of the last hidden layer (or any intermediate layer) is taken as the embedding vector for the input image. You can then measure the similarity between embeddings using a distance metric, such as cosine similarity or Euclidean distance, and recommend images that have the highest similarity scores to the user's uploaded image. Option A is incorrect because downloading a pretrained CNN and fine-tuning the model to predict hashtags based on the input images may not capture the visual similarity of the images, as hashtags may not reflect the appearance of the images accurately. For example, two images of different breeds of dogs may have the same hashtag #dog, but they may not look similar to each other. Moreover, fine-tuning the model may require additional data and computational resources, and it may not generalize well to new images that have different or missing hashtags. Option B is incorrect because retrieving image labels and dominant colors from the input images using the Vision API may not capture the visual similarity of the images, as labels and colors may not reflect the fine-grained details of the images. For example, two images of the same breed of dog may have different labels and colors depending on the background, lighting, and angle of the image. Moreover, using the Vision API may incur additional costs and latency, and it may not be able to handle custom or domain-specific labels. Option C is incorrect because using the provided hashtags to create a collaborative filtering algorithm may not capture the visual similarity of the images, as collaborative filtering relies on the ratings or preferences of users, not the features of the images. For example, two images of different animals may have similar ratings or preferences from users, but they may not look similar to each other. Moreover, collaborative filtering may suffer from the cold start problem, where new images or users that have no ratings or preferences cannot be recommended. Reference:
Image similarity search with TensorFlow
Image embeddings documentation
Pretrained models documentation
Similarity metrics documentation

NEW QUESTION # 55
......

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