THREE FORMATS OF ITDUMPSFREE PRACTICE MATERIAL

Three Formats of ITdumpsfree Practice Material

Three Formats of ITdumpsfree Practice Material

Blog Article

Tags: Reliable Professional-Machine-Learning-Engineer Braindumps Questions, Valid Dumps Professional-Machine-Learning-Engineer Ebook, Professional-Machine-Learning-Engineer Test Discount, Professional-Machine-Learning-Engineer Cert Exam, Latest Professional-Machine-Learning-Engineer Exam Tips

What's more, part of that ITdumpsfree Professional-Machine-Learning-Engineer dumps now are free: https://drive.google.com/open?id=1KwmMCjMHaxjXJ8MjuCjoKyVy_mBnMbAL

ITdumpsfree insists on providing you with the best and high quality exam dumps, aiming to ensure you 100% pass in the actual test. Being qualified with Google certification will bring you benefits beyond your expectation. Our Google Professional-Machine-Learning-Engineer practice training material will help you to enhance your specialized knowledge and pass your actual test with ease. Professional-Machine-Learning-Engineer Questions are all checked and verified by our professional experts. Besides, the Professional-Machine-Learning-Engineer answers are all accurate which ensure the high hit rate.

Professional Machine Learning Engineer - Google Certified salary

The estimated average salary of Professional Machine Learning Engineer - Google is listed below:

  • India: 8,580,000 INR
  • Europe: 97,000 EURO
  • United States: 114,000 USD
  • England: 87,200 POUND

>> Reliable Professional-Machine-Learning-Engineer Braindumps Questions <<

Pass Guaranteed Quiz Google - Latest Reliable Professional-Machine-Learning-Engineer Braindumps Questions

With our users all over the world, you really should believe in the choices of so many people. Our advantage is very obvious. Of course, the right to choose is in your hands. What I want to say is that if you are eager to get an international Professional-Machine-Learning-Engineer Certification, you must immediately select our Professional-Machine-Learning-Engineer preparation materials. After you have studied for twenty to thirty hours on our Professional-Machine-Learning-Engineer exam questions, you can take the test. And your pass rate will reach 99%.

Artificial Intelligence is significantly shaping the world as we know it, from personal virtual assistants to self-driving cars. Hence, having a Google Professional Machine Learning Engineer certification induces offers of exciting career paths with high-paid salaries. Google Professional Machine Learning Engineer certification offers knowledge and industry recognition of abilities that enable one to design, develop, productionalize, and monitor ML models and coordinate with across teams.

The Google Professional-Machine-Learning-Engineer Exam is designed to test a variety of skills and knowledge areas related to machine learning, including data analysis, model selection and evaluation, and deployment and monitoring of machine learning models. It is also designed to test candidates' ability to apply machine learning techniques to real-world problems and to demonstrate their ability to work effectively with data science teams.

Google Professional Machine Learning Engineer Sample Questions (Q242-Q247):

NEW QUESTION # 242
You have created a Vertex Al pipeline that includes two steps. The first step preprocesses 10 TB data completes in about 1 hour, and saves the result in a Cloud Storage bucket The second step uses the processed data to train a model You need to update the model's code to allow you to test different algorithms You want to reduce pipeline execution time and cost, while also minimizing pipeline changes What should you do?

  • A. Add a pipeline parameter and an additional pipeline step Depending on the parameter value the pipeline step conducts or skips data preprocessing and starts model training.
  • B. Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.
  • C. Enable caching for the pipeline job. and disable caching for the model training step.
  • D. Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.

Answer: C

Explanation:
The best option for reducing pipeline execution time and cost, while also minimizing pipeline changes, is to enable caching for the pipeline job, and disable caching for the model training step. This option allows you to leverage the power and simplicity of Vertex AI Pipelines to reuse the output of the data preprocessing step, and avoid unnecessary recomputation. Vertex AI Pipelines is a service that can orchestrate machine learning workflows using Vertex AI. Vertex AI Pipelines can run preprocessing and training steps on custom Docker images, and evaluate, deploy, and monitor the machine learning model. Caching is a feature of Vertex AI Pipelines that can store and reuse the output of a pipeline step, and skip the execution of the step if the input parameters and the code have not changed. Caching can help you reduce the pipeline execution time and cost, as you do not need to re-run the same step with the same input and code. Caching can also help you minimize the pipeline changes, as you do not need to add or remove any pipeline steps or parameters. By enabling caching for the pipeline job, and disabling caching for the model training step, you can create a Vertex AI pipeline that includes two steps. The first step preprocesses 10 TB data, completes in about 1 hour, and saves the result in a Cloud Storage bucket. The second step uses the processed data to train a model. You can update the model's code to allow you to test different algorithms, and run the pipeline job with caching enabled. The pipeline job will reuse the output of the data preprocessing step from the cache, and skip the execution of the step. The pipeline job will run the model training step with the updated code, and disable the caching for the step. This way, you can reduce the pipeline execution time and cost, while also minimizing pipeline changes1.
The other options are not as good as option D, for the following reasons:
* Option A: Adding a pipeline parameter and an additional pipeline step, depending on the parameter value, the pipeline step conducts or skips data preprocessing and starts model training, would require more skills and steps than enabling caching for the pipeline job, and disabling caching for the model training step. A pipeline parameter is a variable that can be used to control the input or output of a pipeline step. A pipeline parameter can help you customize the pipeline logic and behavior, and experiment with different values. An additional pipeline step is a new instance of a pipeline component that can perform a part of the pipeline workflow, such as data preprocessing or model training. An additional pipeline step can help you extend the pipeline functionality and complexity, and handle different scenarios. However, adding a pipeline parameter and an additional pipeline step, depending on the parameter value, the pipeline step conducts or skips data preprocessing and starts model training, would require more skills and steps than enabling caching for the pipeline job, and disabling caching for the model training step. You would need to write code, define the pipeline parameter, create the additional pipeline step, implement the conditional logic, and compile and run the pipeline. Moreover, this option would not reuse the output of the data preprocessing step from the cache, but rather from the Cloud Storage bucket, which can increase the data transfer and access costs1.
* Option B: Creating another pipeline without the preprocessing step, and hardcoding the preprocessed Cloud Storage file location for model training, would require more skills and steps than enabling caching for the pipeline job, and disabling caching for the model training step. A pipeline without the preprocessing step is a pipeline that only includes the model training step, and uses the preprocessed data from the Cloud Storage bucket as the input. A pipeline without the preprocessing step can help you avoid running the data preprocessing step every time, and reduce the pipeline execution time and cost.
However, creating another pipeline without the preprocessing step, and hardcoding the preprocessed Cloud Storage file location for model training, would require more skills and steps than enabling caching for the pipeline job, and disabling caching for the model training step. You would need to write code, create a new pipeline, remove the preprocessing step, hardcode the Cloud Storage file location, and compile and run the pipeline. Moreover, this option would not reuse the output of the data preprocessing step from the cache, but rather from the Cloud Storage bucket, which can increase the data transfer and access costs. Furthermore, this option would create another pipeline, which can increase the maintenance and management costs1.
* Option C: Configuring a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step, would not reduce the pipeline execution time and cost, while also minimizing pipeline changes, but rather increase the pipeline execution cost and complexity. A machine with more CPU and RAM from the compute-optimized machine family is a virtual machine that has a high ratio of CPU cores to memory, and can provide high performance and scalability for compute-intensive workloads. A machine with more CPU and RAM from the compute-optimized machine family can help you optimize the data preprocessing step, and reduce the pipeline execution time. However, configuring a machine with more CPU and RAM from the compute-optimized machine
* family for the data preprocessing step, would not reduce the pipeline execution time and cost, while also minimizing pipeline changes, but rather increase the pipeline execution cost and complexity. You would need to write code, configure the machine type parameters for the data preprocessing step, and compile and run the pipeline. Moreover, this option would increase the pipeline execution cost, as machines with more CPU and RAM from the compute-optimized machine family are more expensive than machines with less CPU and RAM from other machine families. Furthermore, this option would not reuse the output of the data preprocessing step from the cache, but rather re-run the data preprocessing step every time, which can increase the pipeline execution time and cost1.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 3: MLOps
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.2 Automating ML workflows
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.4: Automating ML Workflows
* Vertex AI Pipelines
* Caching
* Pipeline parameters
* Machine types


NEW QUESTION # 243
You received a training-serving skew alert from a Vertex Al Model Monitoring job running in production.
You retrained the model with more recent training data, and deployed it back to the Vertex Al endpoint but you are still receiving the same alert. What should you do?

  • A. Temporarily disable the alert Enable the alert again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint.
  • B. Update the model monitoring job to use the more recent training data that was used to retrain the model.
  • C. Temporarily disable the alert until the model can be retrained again on newer training data Retrain the model again after a sufficient amount of new production traffic has passed through the Vertex Al endpoint
  • D. Update the model monitoring job to use a lower sampling rate.

Answer: B

Explanation:
The best option for resolving the training-serving skew alert is to update the model monitoring job to use the more recent training data that was used to retrain the model. This option can help align the baseline distribution of the model monitoring job with the current distribution of the production data, and eliminate the false positive alerts. Model Monitoring is a service that can track and compare the results of multiple machine learning runs. Model Monitoring can monitor the model's prediction input data for feature skew and drift.
Training-serving skew occurs when the feature data distribution in production deviates from the feature data distribution used to train the model. If the original training data is available, you can enable skew detection to monitor your models for training-serving skew. Model Monitoring uses TensorFlow Data Validation (TFDV) to calculate the distributions and distance scores for each feature, and compares them with a baseline distribution. The baseline distribution is the statistical distribution of the feature's values in the training data. If the distance score for a feature exceeds an alerting threshold that you set, Model Monitoring sends you an email alert. However, if you retrain the model with more recent training data, and deploy it back to the Vertex AI endpoint, the baseline distribution of the model monitoring job may become outdated and inconsistent with the current distribution of the production data. This can cause the model monitoring job to generate false positive alerts, even if the model performance is not deteriorated. To avoid this problem, you need to update the model monitoring job to use the more recent training data that was used to retrain the model. This can help the model monitoring job to recalculate the baseline distribution and the distance scores, and compare them with the current distribution of the production data. This can also help the model monitoring job to detect any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade1.
The other options are not as good as option B, for the following reasons:
* Option A: Updating the model monitoring job to use a lower sampling rate would not resolve the training-serving skew alert, and could reduce the accuracy and reliability of the model monitoring job.
The sampling rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a lower sampling rate can reduce the storage and computation costs of the model monitoring job, but also the quality and validity of the data. Using a lower sampling rate can introduce sampling bias and noise into the data, and make the model monitoring job miss some important features or patterns of the data. Moreover, using a lower sampling rate would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data2.
* Option C: Temporarily disabling the alert, and enabling the alert again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint, would not resolve the training-serving skew alert, and could expose the model to potential risks and errors. Disabling the alert would stop the model monitoring job from sending email notifications when the distance score for a feature exceeds the alerting threshold, but it would not stop the model monitoring job from calculating and comparing the distributions and distance scores. Therefore, disabling the alert would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data. Moreover, disabling the alert would prevent the model monitoring job from detecting any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade. This can expose the model to potential risks and errors, and affect the user satisfaction and trust1.
* Option D: Temporarily disabling the alert until the model can be retrained again on newer training data, and retraining the model again after a sufficient amount of new production traffic has passed through the Vertex AI endpoint, would not resolve the training-serving skew alert, and could cause unnecessary costs and efforts. Disabling the alert would stop the model monitoring job from sending email notifications when the distance score for a feature exceeds the alerting threshold, but it would not stop the model monitoring job from calculating and comparing the distributions and distance scores.
Therefore, disabling the alert would not address the root cause of the training-serving skew alert, which is the mismatch between the baseline distribution and the current distribution of the production data.
Moreover, disabling the alert would prevent the model monitoring job from detecting any true positive alerts, such as a sudden change in the production data that causes the model performance to degrade.
This can expose the model to potential risks and errors, and affect the user satisfaction and trust.
Retraining the model again on newer training data would create a new model version, but it would not
* update the model monitoring job to use the newer training data as the baseline distribution. Therefore, retraining the model again on newer training data would not resolve the training-serving skew alert, and could cause unnecessary costs and efforts1.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 4: Evaluation
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.3 Monitoring ML models in production
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.3: Monitoring ML Models
* Using Model Monitoring
* Understanding the score threshold slider
* Sampling rate


NEW QUESTION # 244
Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.
1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.
2 Predictions will be stored in BigQuery
3. The model will be stored in a Cloud Storage bucket and will be updated frequently You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

  • A. Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.
  • B. Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
  • C. Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.
  • D. Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.

Answer: C


NEW QUESTION # 245
You built a deep learning-based image classification model by using on-premises dat a. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

  • A. Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.
  • B. Use Vertex Explainable Al for model explainability Configure example-based explanations.
  • C. Use Vertex Explainable Al for model explainability Configure feature-based explanations.
  • D. Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.

Answer: D

Explanation:
Vertex AI Model Monitoring is a service that allows you to monitor the performance and quality of your ML models in production. You can use Vertex AI Model Monitoring to detect changes in the input data distribution, the prediction output distribution, or the model accuracy over time. Training-serving skew detection is a feature of Vertex AI Model Monitoring that compares the statistics of the data used for training the model and the data used for serving the model. If there is a significant difference between the two data distributions, it indicates that the model might be outdated or inaccurate. By enabling training-serving skew detection for your model, you can detect model performance changes in production and trigger retraining or redeployment of your model as needed. This way, you can ensure that your model is always up-to-date and accurate, without moving your data to the cloud. Reference:
Vertex AI Model Monitoring documentation
Training-serving skew detection documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate


NEW QUESTION # 246
You are developing a training pipeline for a new XGBoost classification model based on tabular data The data is stored in a BigQuery table You need to complete the following steps
1. Randomly split the data into training and evaluation datasets in a 65/35 ratio
2. Conduct feature engineering
3 Obtain metrics for the evaluation dataset.
4 Compare models trained in different pipeline executions
How should you execute these steps'?

  • A. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
    2 Enable autologging of metrics in the training component
    3 Compare models using the artifacts lineage in Vertex ML Metadata
  • B. 1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model type and use BigQuery to handle the data splits.
    2 Use a SQL view to apply feature engineering and train the model using the data in that view
    3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement.
  • C. 1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering
    2. Enable auto logging of metrics in the training component.
    3 Compare pipeline runs in Vertex Al Experiments
  • D. 1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model type, and use BigQuery to handle the data splits.
    2 Use ml transform to specify the feature engineering transformations, and train the model using the data in the table

Answer: A

Explanation:
' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.
Explanation:
Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud. You can use Vertex AI Pipelines to add a component to divide the data into training and evaluation sets, and add another component for feature engineering. A component is a self-contained piece of code that performs a specific task in the pipeline. You can use the built-in components provided by Vertex AI Pipelines, or create your own custom components. By using Vertex AI Pipelines, you can orchestrate and automate your ML workflow, and track the provenance and lineage of your data and models. You can also enable autologging of metrics in the training component, which is a feature that automatically logs the metrics from your XGBoost model to Vertex AI Experiments. 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 monitor the training progress, visualize the metrics, and analyze the results of your model. You can also compare models using the artifacts lineage in Vertex ML Metadata. Vertex ML Metadata is a service that stores and manages the metadata of your ML artifacts, such as datasets, models, metrics, and executions. You can use Vertex ML Metadata to view the artifacts lineage, which is a graph that shows the relationships and dependencies among the artifacts. By using the artifacts lineage, you can compare the performance and quality of different models trained in different pipeline executions, and identify the best model for your use case. By using Vertex AI Pipelines, Vertex AI Experiments, and Vertex ML Metadata, you can execute the steps required for developing a training pipeline for a new XGBoost classification model based on tabular data stored in a BigQuery table. Reference:
Vertex AI Pipelines documentation
Vertex AI Experiments documentation
Vertex ML Metadata documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate


NEW QUESTION # 247
......

Valid Dumps Professional-Machine-Learning-Engineer Ebook: https://www.itdumpsfree.com/Professional-Machine-Learning-Engineer-exam-passed.html

BONUS!!! Download part of ITdumpsfree Professional-Machine-Learning-Engineer dumps for free: https://drive.google.com/open?id=1KwmMCjMHaxjXJ8MjuCjoKyVy_mBnMbAL

Report this page