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Professional-Machine-Learning-Engineer Interactive Course, Professional-Machine-Learning-Engineer Valid Test Braindumps

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Google Professional Machine Learning Engineer Certification Exam is an essential credential for professionals seeking to advance their careers in machine learning. It provides a comprehensive assessment of a candidate's knowledge and skills in designing and implementing machine learning models and systems on the Google Cloud Platform. With the demand for machine learning professionals on the rise, obtaining this certification can open up new opportunities for career growth and advancement.

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.

To be eligible to take the exam, candidates must have a strong understanding of machine learning concepts, including supervised and unsupervised learning, deep learning, and reinforcement learning. They must also have experience working with Google Cloud's machine learning services, such as AutoML, AI Platform, and TensorFlow.

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Google Professional Machine Learning Engineer Sample Questions (Q155-Q160):

NEW QUESTION # 155
You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

A. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.
B. Create a hot-encoding of words, and feed the encodings into your model.
C. Identify word embeddings from a pre-trained model, and use the embeddings in your model.
D. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.

Answer: C

Explanation:
* Option A is incorrect because creating a one-hot encoding of words, and feeding the encodings into your model is not an efficient way to preprocess the words individually for a natural language model. One-hot encoding is a method of representing categorical variables as binary vectors, where each element corresponds to a category and only one element is 1 andthe rest are 01. However, this method is not suitable for high-dimensional and sparse data, such as words in a large vocabulary, because it requires a lot of memory and computation, and does not capture the semantic similarity or relationship between words2.
* Option B is correct because identifying word embeddings from a pre-trained model, and using the embeddings in your model is a good way to preprocess the words individually for a natural language model. Word embeddings are low-dimensional and dense vectors that represent the meaning and usage of words in a continuous space3. Word embeddings can be learned from a large corpus of text using neural networks, such as word2vec, GloVe, or BERT4. Using pre-trained word embeddings can save time and resources, and improve the performance of the natural language model, especially when the training data is limited or noisy5.
* Option C is incorrect because sorting the words by frequency of occurrence, and using the frequencies as the encodings in your model is not a meaningful way to preprocess the words individually for a natural language model. This method implies that the frequency of a wordis a good indicator of its importance or relevance, which may not be true. For example, the word "the" is very frequent but not very informative, while the word "unicorn" is rare but more distinctive. Moreover, this method does not capture the semantic similarity or relationship between words, and may introduce noise or bias into the model.
* Option D is incorrect because assigning a numerical value to each word from 1 to 100,000 and feeding the values as inputs in your model is not a valid way to preprocess the words individually for a natural language model. This method implies an ordinal relationship between the words, which may not be true.
For example, assigning the values 1, 2, and 3 to the words "apple", "banana", and "orange" does not
* make sense, as there is no inherent order among these fruits. Moreover, this method does not capture the semantic similarity or relationship between words, and may confuse the model with irrelevant or misleading information.
References:
* One-hot encoding
* Word embeddings
* Word embedding
* Pre-trained word embeddings
* Using pre-trained word embeddings in a Keras model
* [Term frequency]
* [Term frequency-inverse document frequency]
* [Ordinal variable]
* [Encoding categorical features]

NEW QUESTION # 156
You need to build classification workflows over several structured datasets currently stored in BigQuery.
Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

A. Run a BigQuery ML task to perform logistic regression for the classification
B. Use Al Platform to run the classification model job configured for hyperparameter tuning
C. Configure AutoML Tables to perform the classification task
D. Use Al Platform Notebooks to run the classification model with pandas library

Answer: C

Explanation:
AutoML Tables is a service that allows you to automatically build and deploy state-of-the-art machine learning models on structured data without writing code. You can use AutoML Tables to perform the following steps for the classification task:
* Exploratory data analysis: AutoML Tables provides a graphical user interface (GUI) and a command-line interface (CLI) to explore your data, visualize statistics, and identify potential issues.
* Feature selection: AutoML Tables automatically selects the most relevant features for your model based on the data schema and the target column. You can also manually exclude or include features, or create new features from existing ones using feature engineering.
* Model building: AutoML Tables automatically builds and evaluates multiple machine learning models using different algorithms and architectures. You can also specify the optimization objective, the budget, and the evaluation metric for your model.
* Training and hyperparameter tuning: AutoML Tables automatically trains and tunes your model using the best practices and techniques from Google's research and engineering teams. You can monitor the training progress and the performance of your model on the GUI or the CLI.
* Serving: AutoML Tables automatically deploys your model to a fully managed, scalable, and secure environment. You can use the GUI or the CLI to request predictions from your model, either online
* (synchronously) or offline (asynchronously).
References:
* [AutoML Tables documentation]
* [AutoML Tables overview]
* [AutoML Tables how-to guides]

NEW QUESTION # 157
You work on a growing team of more than 50 data scientists who all use Al Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
B. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources
C. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about Al Platform resource usage In BigQuery create a SQL view that maps users to the resources they are using.
D. Set up restrictive I AM permissions on the Al Platform notebooks so that only a single user or group can access a given instance.

Answer: B

Explanation:
Labels are key-value pairs that can be attached to any AI Platform resource, such as jobs, models, versions, or endpoints1. Labels can help you organize your resources into descriptive categories, such as project, team, environment, or purpose. You can use labels to filter the results when you list or monitor your resources, or to group them for billing or quota purposes2. Using labels is a simple and scalable way to manage your AI Platform resources without creating unnecessary complexity or overhead. Therefore, using labels to organize resources is the best strategy for this use case.
Reference:
Using labels
Filtering and grouping by labels

NEW QUESTION # 158
Your team trained and tested a DNN regression model with good results. Six months after deployment, the model is performing poorly due to a change in the distribution of the input dat a. How should you address the input differences in production?

A. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features
B. Create alerts to monitor for skew, and retrain the model.
C. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
D. Perform feature selection on the model, and retrain the model with fewer features

Answer: C

NEW QUESTION # 159
You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%. How can you make your production model more accurate?

A. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
B. Split the training and test data based on time rather than a random split to avoid leakage
C. Add more data to your test set to ensure that you have a fair distribution and sample for testing
D. Normalize the data for the training, and test datasets as two separate steps.

Answer: C

NEW QUESTION # 160
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