Professional-Machine-Learning-Engineer Certification Dump - Professional-Machine-Learning-Engineer Exam Pass4sure

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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

SectionObjectives
Designing ML solutions- ML architecture design
  • 1. Select appropriate ML models and approaches
    • 2. Design scalable ML systems on GCP
      - Framing ML problems
      • 1. Define success metrics and evaluation criteria
        • 2. Translate business problems into ML tasks
          Deployment and operations- Model deployment
          • 1. Batch and online prediction systems
            • 2. Deploy models using Vertex AI endpoints
              - Monitoring and maintenance
              • 1. Monitor model drift and performance
                • 2. Retraining and lifecycle management
                  ML model development- Model training and tuning
                  • 1. Train models using TensorFlow / Vertex AI
                    • 2. Hyperparameter tuning and optimization
                      - Evaluation
                      • 1. Model validation strategies
                        • 2. Evaluate model performance metrics
                          ML pipeline automation and orchestration- Pipeline design
                          • 1. Build end-to-end ML pipelines
                            • 2. Use Vertex AI Pipelines
                              Data preparation and processing- Data ingestion and pipelines
                              • 1. Use BigQuery and data processing services
                                • 2. Build data pipelines for training and serving
                                  - Feature engineering
                                  • 1. Transform and preprocess datasets
                                    • 2. Feature selection and representation techniques

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                                      Google Professional Machine Learning Engineer Sample Questions (Q226-Q231):

                                      NEW QUESTION # 226
                                      You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?

                                      Answer: D


                                      NEW QUESTION # 227
                                      You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?

                                      Answer: C

                                      Explanation:
                                      AutoML Natural Language is a service that allows users to create custom natural language models using their own data and labels. It supports various natural language tasks, such as text classification, entity extraction, and sentiment analysis. AutoML Natural Language can be used to build a model to classify incoming calls by product, as it can extract custom entities from the transcribed calls and assign them to predefined categories. AutoML Natural Language also minimizes data preprocessing and development time, as it handles the data preparation, model training, and evaluation automatically. The other options are not as suitable for this scenario. AI Platform Training built-in algorithms are a set of pre-defined algorithms that can be used to train ML models on AI Platform, but they do not support natural language processing tasks. Cloud Natural Language API is a pre-trained service that provides natural language understanding capabilities, such as sentiment analysis, entity analysis, syntax analysis, and content classification. However, it does not support custom entities or categories, and may not recognize the product names from the calls. Building a custom model to identify the product keywords and then running them through a classification algorithm would require more data preprocessing and development time, as well as more coding and testing. Reference:
                                      AutoML Natural Language documentation
                                      AI Platform Training built-in algorithms documentation
                                      Cloud Natural Language API documentation


                                      NEW QUESTION # 228
                                      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?

                                      Answer: A

                                      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 # 229
                                      You are building a custom image classification model and plan to use Vertex Al Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline'?

                                      Answer: A


                                      NEW QUESTION # 230
                                      You work for a telecommunications company You're building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery, and the predictive features that are available for model training include
                                      - Customer_id -Age
                                      - Salary (measured in local currency) -Sex
                                      -Average bill value (measured in local currency)
                                      - Number of phone calls in the last month (integer) -Average duration of phone calls (measured in minutes) You need to investigate and mitigate potential bias against disadvantaged groups while preserving model accuracy What should you do?

                                      Answer: B


                                      NEW QUESTION # 231
                                      ......

                                      Candidates who crack the Professional-Machine-Learning-Engineer examination of the Google Professional-Machine-Learning-Engineer certification validate their worth in the sector of information technology. The Google Professional-Machine-Learning-Engineer credential is evidence of their talent. Reputed firms hire these talented people for high-paying jobs. To get the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) certification, it is essential to clear the Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) test. For this task, you need to update Google Professional Machine Learning Engineer (Professional-Machine-Learning-Engineer) preparation material to get success.

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