GET HELP FROM REAL GOOGLE PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM QUESTIONS IN PREPARATION

Get Help From Real Google Professional-Machine-Learning-Engineer Exam Questions in Preparation

Get Help From Real Google Professional-Machine-Learning-Engineer Exam Questions in Preparation

Blog Article

Tags: Reliable Professional-Machine-Learning-Engineer Exam Cost, Professional-Machine-Learning-Engineer Latest Exam Forum, Professional-Machine-Learning-Engineer PDF, Professional-Machine-Learning-Engineer Practice Exam Pdf, Professional-Machine-Learning-Engineer Real Torrent

BTW, DOWNLOAD part of DumpStillValid Professional-Machine-Learning-Engineer dumps from Cloud Storage: https://drive.google.com/open?id=1Eo5HNeJs3PYEULY2KIKz3PFcJF6QVz9_

Our company DumpStillValid abides by the industry norm all the time. By virtue of the help from professional experts, who are conversant with the regular exam questions of our latest Professional-Machine-Learning-Engineer real dumps. They can satisfy your knowledge-thirsty minds. And our Professional-Machine-Learning-Engineer Exam Quiz is quality guaranteed. By devoting ourselves to providing high-quality Professional-Machine-Learning-Engineer practice materials to our customers all these years we can guarantee all content is of the essential part to practice and remember.

With over a decade’s endeavor, our Professional-Machine-Learning-Engineer practice materials successfully become the most reliable products in the industry. There is a great deal of advantages of our Professional-Machine-Learning-Engineer exam questions you can spare some time to get to know. You can visit our website, and chat with our service online or via email at any time for we are working 24/7 online. Or you can free download the demos of our Professional-Machine-Learning-Engineer learning guide on our website, just click on the buttons, you can reach whatever you want to know.

>> Reliable Professional-Machine-Learning-Engineer Exam Cost <<

Google Professional-Machine-Learning-Engineer Latest Exam Forum & Professional-Machine-Learning-Engineer PDF

In today's rapidly changing Google industry, the importance of obtaining Google Professional-Machine-Learning-Engineer certification has become increasingly evident. With the constant evolution of technology, staying competitive in the job market requires professionals to continuously upgrade their skills and knowledge. The DumpStillValid is committed to completely assisting you in exam preparation with Professional-Machine-Learning-Engineer Questions. Success in the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) certification exam is crucial in the tech sector, where the stakes are high, and a single mistake can have significant consequences.

Google Professional Machine Learning Engineer Sample Questions (Q90-Q95):

NEW QUESTION # 90
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. Downsample the data with upweighting to create a sample with 10% positive examples
  • D. Use a convolutional neural network with max pooling and softmax activation

Answer: C

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 # 91
You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than 20 languages and are translated in real time using the Cloud Translation API. You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?

  • A. Add a regularization term such as the Min-Diff algorithm to the loss function.
  • B. Replace the in-house word2vec with GPT-3 or T5.
  • C. Remove moderation for languages for which the false positive rate is too high.
  • D. Train a classifier using the chat messages in their original language.

Answer: C


NEW QUESTION # 92
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?

  • A. Change the partitioning step to reduce the dimension of the test set and have a larger training set.
  • B. Improve the data cleaning step by removing features with missing values.
  • C. Use feature construction to combine the strongest features.
  • D. Use the representation transformation (normalization) technique.

Answer: D

Explanation:
https://developers.google.com/machine-learning/data-prep/transform/transform-numeric
- NN models needs features with close ranges
- SGD converges well using features in [0, 1] scale
- The question specifically mention "different ranges"
Documentation - https://developers.google.com/machine-learning/data-prep/transform/transform-numeric


NEW QUESTION # 93
You are developing a recommendation engine for an online clothing store. The historical customer transaction data is stored in BigQuery and Cloud Storage. You need to perform exploratory data analysis (EDA), preprocessing and model training. You plan to rerun these EDA, preprocessing, and training steps as you experiment with different types of algorithms. You want to minimize the cost and development effort of running these steps as you experiment. How should you configure the environment?

  • A. Create a Vertex Al Workbench managed notebook to browse and query the tables directly from the JupyterLab interface.
  • B. Create a Vertex Al Workbench user-managed notebook on a Dataproc Hub. and use the %%bigquery magic commands in Jupyter to query the tables.
  • C. Create a Vertex Al Workbench managed notebook on a Dataproc cluster, and use the spark-bigquery- connector to access the tables.
  • D. Create a Vertex Al Workbench user-managed notebook using the default VM instance, and use the %% bigquery magic commands in Jupyter to query the tables.

Answer: D

Explanation:
* Cost-effectiveness: User-managed notebooks in Vertex AI Workbench allow you to leverage pre- configured virtual machines with reasonable resource allocation, keeping costs lower compared to options involving managed notebooks or Dataproc clusters.
* Development flexibility: User-managed notebooks offer full control over the environment, allowing you to install additional libraries or dependencies needed for your specific EDA, preprocessing, and model training tasks. This flexibility is crucial while experimenting with different algorithms.
* BigQuery integration: The %%bigquery magic commands provide seamless integration with BigQuery within the Jupyter Notebook environment. This enables efficient querying and exploration of customer transaction data stored in BigQuery directly from the notebook, streamlining the workflow.
Other options and why they are not the best fit:
* B. Managed notebook: While managed notebooks offer an easier setup, they might have limited customization options, potentially hindering your ability to install specific libraries or tools.
* C. Dataproc Hub: Dataproc Hub focuses on running large-scale distributed workloads, and it might be overkill for your scenario involving exploratory analysis and experimentation with different algorithms.
Additionally, it could incur higher costs compared to a user-managed notebook.
* D. Dataproc cluster with spark-bigquery-connector: Similar to option C, using a Dataproc cluster with the spark-bigquery-connector would be more complex and potentially more expensive than using %% bigquery magic commands within a user-managed notebook for accessing BigQuery data.
References:
* https://cloud.google.com/vertex-ai/docs/workbench/instances/bigquery
* https://cloud.google.com/vertex-ai-notebooks


NEW QUESTION # 94
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance? (Choose One Correct Answer)

  • A. User engagement as measured by the number of battles played daily per user
  • B. Average time players wait before being assigned to a team
  • C. Precision and recall of assigning players to teams based on their predicted versus actual ability
  • D. Rate of return as measured by additional revenue generated minus the cost of developing a new model

Answer: A

Explanation:
The best business metric to track to measure the model's performance is user engagement as measured by the number of battles played daily per user. This metric reflects the main goal of the model, which is to enhance the user experience and satisfaction by creating balanced and fair battles. If the model is successful, it should increase the user retention and loyalty, as well as the word-of-mouth and referrals. This metric is also easy to measure and interpret, as it can be directly obtained from the user activity data.
The other options are not optimal for the following reasons:
A . Average time players wait before being assigned to a team is not a good metric, as it does not capture the quality or outcome of the battles. It only measures the efficiency of the model, which is not the primary objective. Moreover, this metric can be influenced by external factors, such as the availability and demand of players, the network latency, and the server capacity.
B . Precision and recall of assigning players to teams based on their predicted versus actual ability is not a good metric, as it is difficult to measure and interpret. It requires having a reliable and consistent way of estimating the player's ability, which can be subjective and dynamic. It also requires having a ground truth label for each assignment, which can be costly and impractical to obtain. Moreover, this metric does not reflect the user feedback or satisfaction, which is the ultimate goal of the model.
D . Rate of return as measured by additional revenue generated minus the cost of developing a new model is not a good metric, as it is not directly related to the model's performance. It measures the profitability of the model, which is a secondary objective. Moreover, this metric can be affected by many other factors, such as the market conditions, the pricing strategy, the marketing campaigns, and the competition.
Reference:
Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification How to measure user engagement How to choose the right metrics for your machine learning model


NEW QUESTION # 95
......

Experts hired by Professional-Machine-Learning-Engineer exam questions not only conducted in-depth research on the prediction of test questions, but also made great breakthroughs in learning methods. With Professional-Machine-Learning-Engineer training materials, you can easily memorize all important points of knowledge without rigid endorsements. With Professional-Machine-Learning-Engineer exam torrent, you no longer need to spend money to hire a dedicated tutor to explain it to you, even if you are a rookie of the industry, you can understand everything in the materials without any obstacles. With Professional-Machine-Learning-Engineer Exam Questions, your teacher is no longer one person, but a large team of experts who can help you solve all the problems you have encountered in the learning process.

Professional-Machine-Learning-Engineer Latest Exam Forum: https://www.dumpstillvalid.com/Professional-Machine-Learning-Engineer-prep4sure-review.html

Google Reliable Professional-Machine-Learning-Engineer Exam Cost In fact, it is just a miracle, Google Reliable Professional-Machine-Learning-Engineer Exam Cost This is our guarantee to all customers, Google Reliable Professional-Machine-Learning-Engineer Exam Cost Or you will forget the so-called good, although all kinds of digital device convenient now we read online, but many of us are used by written way to deepen their memory patterns, Purchase our "DumpStillValid Professional-Machine-Learning-Engineer Latest Exam Forum" study material now and get free updates for up to 1 year.

This is also referred to as a wakeup display image" Handsets usually Professional-Machine-Learning-Engineer Latest Exam Forum come with a few sample wallpapers and screensavers preinstalled that you can choose for a wallpaper or screensaver.

Some complex requirements arise even in simplified examples: Professional-Machine-Learning-Engineer The boundary between data essence i.e, In fact, it is just a miracle, This is our guarantee to all customers.

100% Pass Google - Valid Reliable Professional-Machine-Learning-Engineer Exam Cost

Or you will forget the so-called good, although all kinds of Professional-Machine-Learning-Engineer Real Torrent digital device convenient now we read online, but many of us are used by written way to deepen their memory patterns.

Purchase our "DumpStillValid" study material now and get free updates for up to 1 year, We will be responsible for our Professional-Machine-Learning-Engineer valid questions which means the content will continue to update until you have passed the exam.

P.S. Free & New Professional-Machine-Learning-Engineer dumps are available on Google Drive shared by DumpStillValid: https://drive.google.com/open?id=1Eo5HNeJs3PYEULY2KIKz3PFcJF6QVz9_

Report this page