Professional-Machine-Learning-Engineer Exam Questions | Reliable Professional-Machine-Learning-Engineer Exam Materials

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The Google Professional Machine Learning Engineer certification exam covers various topics related to machine learning, such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and deployment. Professionals who pass the exam demonstrate their ability to design and develop machine learning models that meet specific business requirements. Google Professional Machine Learning Engineer certification exam also covers various machine learning techniques such as deep learning, supervised and unsupervised learning, and reinforcement learning.

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Google Professional Machine Learning Engineer Certification Exam is recognized globally as a standard of excellence in the field of machine learning engineering. It is a valuable credential that can enhance the career prospects of individuals by demonstrating their expertise and proficiency in machine learning engineering to potential employers.

The Professional Machine Learning Engineer exam is a performance-based assessment that evaluates the candidate's ability to solve real-world problems using machine learning techniques. Professional-Machine-Learning-Engineer Exam consists of a series of hands-on tasks that require the candidate to demonstrate their understanding of various machine learning concepts and their ability to apply them in practical scenarios. Professional-Machine-Learning-Engineer exam is conducted online and can be taken from anywhere in the world.

Google Professional Machine Learning Engineer Sample Questions (Q113-Q118):

NEW QUESTION # 113
You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic You plan to direct all user traffic to the new model You need to deploy the model with minimal disruption to your application What should you do?

A. 1, Create a new model Set it as the default version Upload the model to Vertex Al Model Registry
2 Deploy the new model to the existing endpoint
B. 1. Create a new endpoint.
2. Create a new model Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version Upload the model to Vertex Al Model Registry
3. Deploy the new model to the new endpoint and set the new model to 100% of the traffic
C. 1 Create a new model Set the parentModel parameter to the model ID of the currently deployed model Upload the model to Vertex Al Model Registry.
2 Deploy the new model to the existing endpoint and set the new model to 100% of the traffic.
D. 1 Create a new endpoint.
2 Create a new model Set it as the default version Upload the model to Vertex Al Model Registry.
3. Deploy the new model to the new endpoint.
4 Update Cloud DNS to point to the new endpoint

Answer: C

NEW QUESTION # 114
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 a preemptible v3-8 TPU
C. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with an NVIDIA P100 GPU
D. A Vertex AI Workbench user-managed notebooks instance running on an n1-standard-16 with 4 NVIDIA P100 GPUs

Answer: C

NEW QUESTION # 115
You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex Al Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

Answer: C

Explanation:
The option A is the most suitable solution for logging data and tracking artifacts from each run of a model development experiment in a Vertex AI Workbench notebook. Vertex AI Workbench is a service that allows you to create and run interactive notebooks on Google Cloud. You can use Vertex AI Workbench to experiment with different preprocessing and modeling approaches for your time series prediction problem.
You can also use the Vertex AI TensorBoard instance and the Vertex AI SDK to create an experiment and associate the TensorBoard instance. TensorBoard is a tool that allows you to visualize and monitor the metrics and artifacts of your ML experiments. You can use the Vertex AI SDK to create an experiment object, which is a logical grouping of runs that share a common objective. You can also use the Vertex AI SDK to associate the experiment object with a TensorBoard instance, which is a managed service that hosts a TensorBoard web app. By using the Vertex AI TensorBoard instance and the Vertex AI SDK, you can easily set up and manage your experiments, and access the TensorBoard web app from the Vertex AI console. You can also use the log_time_series_metrics function and the log_metrics function to log data and track artifacts from each run.
The log_time_series_metrics function is a function that allows you to log the time series data, such as the multivariate time series and the labels, to the TensorBoard instance. The log_metrics function is a function that allows you to log the scalar metrics, such as the loss values, to the TensorBoard instance. By using these functions, you can record the data and artifacts from each run of your experiment, and compare them in the TensorBoard web app. You can also use the TensorBoard web app to visualize the data and artifacts, such as the time series plots, the scalar charts, the histograms, and the distributions. By using the Vertex AI TensorBoard instance, the Vertex AI SDK, and the log functions, you can log data and track artifacts from each run of your experiment in a Vertex AI Workbench notebook. References:
* Vertex AI Workbench documentation
* Vertex AI TensorBoard documentation
* Vertex AI SDK documentation
* log_time_series_metrics function documentation
* log_metrics function documentation
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

NEW QUESTION # 116
You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original model You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?

A. Keep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections
B. Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets
C. Keep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters
D. Separate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs

Answer: B

Explanation:
* Option A is incorrect because it does not separate the training dataset into two tables based on the features, which is necessary to train the two models separately and accurately.
* Option B is incorrect because it does not separate the training dataset into two tables based on the features, and because it uses the same monitoring-config-from parameter for both models, which would not account for the different feature selections.
* Option C is incorrect because it deploys the models to two separate endpoints, which would increase the management effort and complexity of the pipeline.
* Option D is correct because it separates the training dataset into two tables based on the features, which would enable the two models to be trained separately and accurately. It also deploys both models to the same endpoint, which would simplify the pipeline and reduce the management effort. It also submits a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets, which would enable the skew detection to work properly for each model.

NEW QUESTION # 117
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?

A. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
B. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.
C. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
D. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.

Answer: C

Explanation:
The best option for performing hyperparameter tuning on Vertex AI to determine the appropriate embedding dimension and the optimal learning rate is to use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials. This option has the following advantages:
It matches the appropriate scaling type for each hyperparameter, based on their range and distribution. The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LINEAR_SCALE makes sense. The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LOG_SCALE is more suitable.
It maximizes the exploration of the hyperparameter space, by using a large number of parallel trials. Since training time is not a concern, using more trials can help find the best combination of hyperparameters that maximizes model accuracy. The default Bayesian optimization tuning algorithm can efficiently sample the hyperparameter space and converge to the optimal values.
The other options are less optimal for the following reasons:
Option B: Using UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials, reduces the exploration of the hyperparameter space, by using a small number of parallel trials. Since training time is not a concern, using fewer trials can miss some potentially good combinations of hyperparameters that maximize model accuracy. The default Bayesian optimization tuning algorithm can benefit from more trials to sample the hyperparameter space and converge to the optimal values.
Option C: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials, mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution. The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LOG_SCALE is not suitable. The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LINEAR_SCALE makes less sense.
Option D: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials, combines the drawbacks of option B and option C. It mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution, and reduces the exploration of the hyperparameter space, by using a small number of parallel trials.
Reference:
[Vertex AI: Hyperparameter tuning overview]
[Vertex AI: Configuring the hyperparameter tuning job]

NEW QUESTION # 118
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PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM QUESTIONS | RELIABLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM MATERIALS

Professional-Machine-Learning-Engineer Exam Questions | Reliable Professional-Machine-Learning-Engineer Exam Materials