NEW PROFESSIONAL-MACHINE-LEARNING-ENGINEER STUDY NOTES, PROFESSIONAL-MACHINE-LEARNING-ENGINEER LATEST EXAMPREP

New Professional-Machine-Learning-Engineer Study Notes, Professional-Machine-Learning-Engineer Latest Examprep

New Professional-Machine-Learning-Engineer Study Notes, Professional-Machine-Learning-Engineer Latest Examprep

Blog Article

Tags: New Professional-Machine-Learning-Engineer Study Notes, Professional-Machine-Learning-Engineer Latest Examprep, Professional-Machine-Learning-Engineer Valid Exam Questions, Professional-Machine-Learning-Engineer Free Dumps, Professional-Machine-Learning-Engineer Study Center

P.S. Free & New Professional-Machine-Learning-Engineer dumps are available on Google Drive shared by ActualVCE: https://drive.google.com/open?id=1FInMiFpsc7M-62pwcqIt3FFMfZ2IXyNx

ActualVCE Google Professional-Machine-Learning-Engineer pdf questions have been marked as the topmost source for the preparation of Professional-Machine-Learning-Engineer new questions by industry experts. These questions cover every topic in the exam, and they have been verified by Google professionals. Moreover, you can download the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) pdf questions demo to get a better analysis of the exam. By practicing with these questions, you can assess your preparation for the Google Professional-Machine-Learning-Engineer new questions.

Google Professional Machine Learning Engineer Exam is a certification exam that is designed to test the skills and knowledge of professionals who are interested in working with machine learning on the Google Cloud Platform. Professional-Machine-Learning-Engineer exam is intended for individuals who have a strong understanding of machine learning concepts and experience with implementing and deploying machine learning models in production environments.

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

The following will be discussed in Google Professional-Machine-Learning-Engineer Exam Dumps:

  • Choice of framework and model
  • Retraining/redeployment evaluation
  • Modeling techniques given interpretability requirements
  • Transfer learning
  • Productionizing
  • Build a model
  • Tracking metrics during training
  • Hardware accelerators
  • Unit tests for model training and serving

>> New Professional-Machine-Learning-Engineer Study Notes <<

Proven Way to Pass the Google Professional-Machine-Learning-Engineer Exam on the First Attempt

It is a prevailing belief for many people that practice separated from theories are blindfold. Our Professional-Machine-Learning-Engineer learning quiz is a salutary guidance helping you achieve success. The numerous feedbacks from our clients praised and tested our strength on this career, thus our Professional-Machine-Learning-Engineer practice materials get the epithet of high quality and accuracy. We are considered the best ally to our customers who want to pass their Professional-Machine-Learning-Engineer exam by their first attempt and achieve the certification successfully!

Google Professional Machine Learning Engineer Sample Questions (Q225-Q230):

NEW QUESTION # 225
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

  • A. * Stream incoming prediction request data into Cloud Spanner
    * Create a view to abstract your preprocessing logic.
    * Query the view every second for new records
    * Submit a prediction request to Al Platform using the transformed data
    * Write the predictions to an outbound Pub/Sub queue.
  • B. * Validate the accuracy of the model that you trained on preprocessed data
    * Create a new model that uses the raw data and is available in real time
    * Deploy the new model onto Al Platform for online prediction
  • C. * Send incoming prediction requests to a Pub/Sub topic
    * Transform the incoming data using a Dataflow job
    * Submit a prediction request to Al Platform using the transformed data
    * Write the predictions to an outbound Pub/Sub queue
  • D. * Send incoming prediction requests to a Pub/Sub topic
    * Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
    * Implement your preprocessing logic in the Cloud Function
    * Submit a prediction request to Al Platform using the transformed data
    * Write the predictions to an outbound Pub/Sub queue

Answer: D

Explanation:
* Option A is incorrect because creating a new model that uses the raw data and is available in real time would require retraining the model and deploying it again, which is not efficient or scalable.
* Option B is incorrect because using a Dataflow job to transform the incoming data would introduce unnecessary latency and complexity for online prediction, which requires fast and simple processing.
* Option C is incorrect because using Cloud Spanner to stream and query the incoming data would incur high costs and overhead for online prediction, which does not need a relational database.
* Option D is correct because using a Cloud Function to preprocess the data and submit a prediction request to Al Platform is a simple and scalable solution for online prediction, which leverages the serverless and event-driven features of Cloud Functions.


NEW QUESTION # 226
You are building an ML model to detect anomalies in real-time sensor dat a. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

  • A. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable
  • B. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery
  • C. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage
  • D. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

Answer: B


NEW QUESTION # 227
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 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.
  • C. 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
  • D. 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

Answer: B


NEW QUESTION # 228
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?

  • A. One feature obtained as an element-wise product between latitude, longitude, and car type
  • B. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
  • C. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
  • D. Three individual features binned latitude, binned longitude, and one-hot encoded car type

Answer: B


NEW QUESTION # 229
While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

  • A. Remove the rows with missing values, and upsample your dataset by 5%.
  • B. Replace the missing values with a placeholder category indicating a missing value.
  • C. Replace the missing values with the feature's mean.
  • D. Move the rows with missing values to your validation dataset.

Answer: B

Explanation:
The best option for handling missing values in a categorical feature is to replace them with a placeholder category indicating a missing value. This is a type of imputation, which is a method of estimating the missing values based on the observed data. Imputing the missing values with a placeholder category preserves the information that the data is missing, and avoids introducing bias or distortion in the feature distribution. It also allows the machine learning model to learn from the missingness pattern, and potentially use it as a predictor for the target variable. The other options are not suitable for handling missing values in a categorical feature, because:
* Removing the rows with missing values and upsampling the dataset by 5% would reduce the size of the dataset and potentially lose important information. It would also introduce sampling bias and overfitting, as the upsampling process would create duplicate or synthetic observations that do not reflect the true population.
* Replacing the missing values with the feature's mean would not make sense for a categorical feature, as the mean is a numerical measure that does not capture the mode or frequency of the categories. It would also create a new category that does not exist in the original data, and might confuse the machine learning model.
* Moving the rows with missing values to the validation dataset would compromise the validity and reliability of the model evaluation, as the validation dataset would not be representative of the test or production data. It would also reduce the amount of data available for training the model, and might introduce leakage or inconsistency between the training and validation datasets. References:
* Imputation of missing values
* Effective Strategies to Handle Missing Values in Data Analysis
* How to Handle Missing Values of Categorical Variables?
* Google Cloud launches machine learning engineer certification
* Google Professional Machine Learning Engineer Certification
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate


NEW QUESTION # 230
......

The clients only need 20-30 hours to learn the Professional-Machine-Learning-Engineer exam questions and prepare for the test. Many people may complain that we have to prepare for the test but on the other side they have to spend most of their time on their most important things such as their jobs, learning and families. But if you buy our Professional-Machine-Learning-Engineer Study Guide you can both do your most important thing well and pass the Professional-Machine-Learning-Engineer test easily because the preparation for the test costs you little time and energy.

Professional-Machine-Learning-Engineer Latest Examprep: https://www.actualvce.com/Google/Professional-Machine-Learning-Engineer-valid-vce-dumps.html

DOWNLOAD the newest ActualVCE Professional-Machine-Learning-Engineer PDF dumps from Cloud Storage for free: https://drive.google.com/open?id=1FInMiFpsc7M-62pwcqIt3FFMfZ2IXyNx

Report this page