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

Section	Objectives
Topic 1: Scaling prototypes into ML models	- Training at scale (Distributed training, TPUs) - Hyperparameter tuning - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn)
Topic 2: Monitoring ML solutions	- Performance monitoring and drift detection - Model retraining strategies - Logging and alerting (Cloud Monitoring)
Topic 3: Collaborating within and across teams to manage data and models	- Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Data management and governance
Topic 4: Automating and orchestrating ML pipelines	- Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems
Topic 5: Architecting low-code ML solutions	- AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models
Topic 6: Serving and scaling models	- Batch prediction - Hardware accelerators (GPU/TPU) in serving - Online prediction (Vertex AI Prediction) - Model optimization (Quantization, Distillation)

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Google Professional Machine Learning Engineer Sample Questions (Q378-Q383):

NEW QUESTION # 378
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

A. Convert the images to tf.Tensor objects, and then run tf.data.Dataset.from_tensors().
B. Create a tf.data.Dataset.prefetch transformation.
C. Convert the images into TFRecords, store the images in Cloud Storage, and then use the tf.data API to read the images for training.
D. Convert the images to tf.Tensor objects, and then run Dataset.from_tensor_slices().

Answer: C

Explanation:
Cite from Google Pag: to construct a Dataset from data in memory, use
tf.data.Dataset.from_tensors() or tf.data.Dataset.from_tensor_slices(). When input data is stored in a file (not in memory), the recommended TFRecord format, you can use tf.data.TFRecordDataset().
tf.data.Dataset is for data in memory.
tf.data.TFRecordDataset is for data in non-memory storage.

NEW QUESTION # 379
You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

A. Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.
B. Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.
C. Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.
D. Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.

Answer: B

NEW QUESTION # 380
You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?

A. Use BigQuery ML to build a statistical AR1MA_PLUS model.
B. Use Prophet on Vertex Al Training to build a custom model.
C. Use TensorFlow on Vertex Al Training to build a custom model.
D. Use Vertex Al Forecast to build a NN-based model.

Answer: A

Explanation:
According to the web search results, BigQuery ML1 is a service that allows you to create and execute machine learning models in BigQuery using SQL queries. BigQuery ML supports various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, deep neural networks, and time series forecasting1. ARIMA_PLUS2 is a statistical model for time series forecasting that is built in to BigQuery ML. ARIMA_PLUS stands for AutoRegressive Integrated Moving Average with eXogenous regressors. ARIMA_PLUS models the relationship between a target variable and its past values, as well as other external factors that might influence the target variable. ARIMA_PLUS can handle multiple time series, seasonality, holidays, and missing values2. Therefore, option C is the best way to use a solution that can be implemented quickly with minimal effort for the given use case, as it allows you to use SQL queries to build and run a forecasting model in BigQuery without moving the data or writing custom code. The other options are not relevant or optimal for this scenario. Reference:
BigQuery ML
ARIMA_PLUS
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 381
You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?

A. AutoML Natural Language
B. Cloud Natural Language API
C. AI Hub pre-made Jupyter Notebooks
D. AI Platform Training built-in algorithms

Answer: A

Explanation:
AutoML Natural Language is a service that allows you to build and train custom natural language models without writing code. You can use AutoML Natural Language to perform sentiment analysis with custom categories, such as positive, negative, or neutral. You can also use pre-trained models or transfer learning to leverage existing knowledge and reduce the amount of data required to train a model from scratch. AutoML Natural Language provides a user-friendly interface and a powerful AutoML engine that optimizes your model for high predictive performance.
Cloud Natural Language API is a service that provides pre-trained models for common natural language tasks, such as sentiment analysis, entity analysis, and syntax analysis. However, it does not allow you to customize the categories or use your own data for training.
AI Hub pre-made Jupyter Notebooks are interactive documents that contain code, text, and visualizations for various machine learning scenarios. However, they require some coding skills and data preparation to use them effectively.
AI Platform Training built-in algorithms are pre-configured machine learning algorithms that you can use to train models on AI Platform. However, they do not support sentiment analysis as a natural language task.
References:
* AutoML Natural Language documentation
* Cloud Natural Language API documentation
* AI Hub documentation
* AI Platform Training documentation

NEW QUESTION # 382
You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

A. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})
B. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})
C. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
D. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})

Answer: C

NEW QUESTION # 383
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