HIGH-QUALITY NEW PROFESSIONAL-MACHINE-LEARNING-ENGINEER PRACTICE MATERIALS - 100% PASS PROFESSIONAL-MACHINE-LEARNING-ENGINEER EXAM

High-quality New Professional-Machine-Learning-Engineer Practice Materials - 100% Pass Professional-Machine-Learning-Engineer Exam

High-quality New Professional-Machine-Learning-Engineer Practice Materials - 100% Pass Professional-Machine-Learning-Engineer Exam

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Achieving the Google Professional Machine Learning Engineer certification is a significant accomplishment for any individual seeking to validate their expertise in machine learning. Google Professional Machine Learning Engineer certification demonstrates that an individual has the skills and knowledge required to design, build, and deploy machine learning models on Google Cloud Platform. It also enables individuals to differentiate themselves in the job market and opens up new career opportunities.

Google Professional Machine Learning Engineer Sample Questions (Q233-Q238):

NEW QUESTION # 233
You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

  • A. Ensure that training is reproducible
  • B. Ensure that all hyperparameters are tuned
  • C. Ensure that model performance is monitored
  • D. Ensure that feature expectations are captured in the schema

Answer: A

Explanation:
https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/aad9f93b86b7addfea4c419b9100c6cdd26cacea.pdf


NEW QUESTION # 234
You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

  • A. Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.
  • B. Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations
  • C. Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.
  • D. Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.

Answer: B

Explanation:
AutoML Translation is a service that allows you to create and train custom ML models for translating text between different languages. You can use AutoML Translation to train a model that can translate instruction manuals for scientific products to 15 different languages. You can also use Translation Hub to configure a project and use the trained model to translate the documents. Translation Hub is a service that allows you to manage and automate your translation workflows on Google Cloud. You can use Translation Hub to upload the documents to a Cloud Storage bucket, select the source and target languages, and apply the trained model to translate the documents. You can also use Translation Hub to download the translated documents or save them to another Cloud Storage bucket. You can also use human reviewers to evaluate the incorrect translations. Human reviewers are people who can review and correct the translations produced by the ML model. You can use human reviewers to improve the quality and accuracy of the translations, and provide feedback to the ML model. You can use Translation Hub to integrate with third-party human review services, such as Google Translate Community or Appen. By using AutoML Translation, Translation Hub, and human reviewers, you can implement a scalable solution that maximizes accuracy and minimizes operational overhead. You can also include a process to evaluate and fix incorrect translations. Reference:
[AutoML Translation documentation]
[Translation Hub documentation]
[Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]


NEW QUESTION # 235
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. Replace the missing values with a placeholder category indicating a missing value.
  • B. Remove the rows with missing values, and upsample your dataset by 5%.
  • C. Move the rows with missing values to your validation dataset.
  • D. Replace the missing values with the feature's mean.

Answer: A

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 # 236
You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts Your team has assembled a set of annotated images from damage claim documents in the company's database The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to tram models on Google Cloud You need to quickly create an initial model What should you do?

  • A. Create a pipeline in Vertex Al Pipelines and configure the AutoMLTrainingJobRunOp compon it to train a custom object detection model by using the annotated image data.
  • B. Train an object detection model in Vertex Al custom training by using the annotated image data.
  • C. Train an object detection model in AutoML by using the annotated image data.
  • D. Download a pre-trained object detection mode! from TensorFlow Hub Fine-tune the model in Vertex Al Workbench by using the annotated image data.

Answer: C


NEW QUESTION # 237
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. Train a classifier using the chat messages in their original language.
  • B. Remove moderation for languages for which the false positive rate is too high.
  • C. Replace the in-house word2vec with GPT-3 or T5.
  • D. Add a regularization term such as the Min-Diff algorithm to the loss function.

Answer: A

Explanation:
The problem with the current approach is that it relies on the Cloud Translation API to translate the chat messages into a common language before embedding them with the in-house word2vec model. This introduces two sources of error: the translation quality and the word2vec quality. The translation quality may vary across different languages, depending on the availability of data and the complexity of the grammar and vocabulary.
The word2vec quality may also vary depending on the size and diversity of the corpus used to train it. These errors may affect the performance of the classifier that moderates the chat messages, resulting in significant differences across the languages.
A better approach would be to train a classifier using the chat messages in their original language, without relying on the Cloud Translation API or the in-house word2vec model. This way, the classifier can learn the nuances and subtleties of each language, and avoid the errors introduced by the translation and embedding processes. This would also reduce the latency and cost of the moderation system, as it would not need to invoke the Cloud Translation API for every message. To train a classifier using the chat messages in their original language, one could use a multilingual pre-trained model such as mBERT or XLM-R, which can handle multiple languages and domains. Alternatively, one could train a separate classifier for each language, using a monolingual pre-trained model such as BERT or a custom model tailored to the specific language and task.
References:
* Professional ML Engineer Exam Guide
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
* Google Cloud launches machine learning engineer certification
* [mBERT: Bidirectional Encoder Representations from Transformers]
* [XLM-R: Unsupervised Cross-lingual Representation Learning at Scale]
* [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]


NEW QUESTION # 238
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