Google Professional Machine Learning Engineer Exam Questions

The best option for using a managed service to submit training jobs with different frameworks is to use Vertex AI Training. Vertex AI Training is a fully managed service that allows you to train custom models on Google Cloud using any framework, such as TensorFlow, PyTorch, scikit-learn, XGBoost, etc. You can also use custom containers to run your own libraries and dependencies. Vertex AI Training handles the infrastructure provisioning, scaling, and monitoring for you, so you can focus on your model development and optimization. Vertex AI Training also integrates with other Vertex AI services, such as Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Prediction. The other options are not as suitable for using a managed service to submit training jobs with different frameworks, because:

Configuring Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob would require more infrastructure maintenance, as Kubeflow is not a fully managed service, and you would have to provision and manage your own Kubernetes cluster. This would also incur more costs, as you would have to pay for the cluster resources, regardless of the training job usage. TFJob is also mainly designed for TensorFlow models, and might not support other frameworks as well as Vertex AI Training.

Creating a library of VM images on Compute Engine, and publishing these images on a centralized repository would require more development time and effort, as you would have to create and maintain different VM images for different frameworks and libraries. You would also have to manually configure and launch the VMs for each training job, and handle the scaling and monitoring yourself. This would not leverage the benefits of a managed service, such as Vertex AI Training.

Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure would require more configuration and administration, as Slurm is not a native Google Cloud service, and you would have to install and manage it on your own VMs or clusters. Slurm is also a general-purpose workload manager, and might not have the same level of integration and optimization for ML frameworks and libraries as Vertex AI Training.Reference:

Vertex AI Training | Google Cloud

Kubeflow on Google Cloud | Google Cloud

TFJob for training TensorFlow models with Kubernetes | Kubeflow

Compute Engine | Google Cloud

Slurm Workload Manager

Kubeflow Pipelines is a service that allows you to create and run machine learning workflows on Google Cloud using various features, model architectures, and hyperparameters.You can use Kubeflow Pipelines to scale up your workflows, leverage distributed training, and access specialized hardware such as GPUs and TPUs1. An experiment in Kubeflow Pipelines is a workspace where you can try different configurations of your pipelines and organize your runs into logical groups.You can use experiments to compare the performance of different models and track the evaluation metrics in the same dashboard2.

For the use case of designing a customized deep neural network in Keras that will predict customer purchases based on their purchase history, the best option is to create an experiment in Kubeflow Pipelines to organize multiple runs. This option allows you to explore model performance using multiple model architectures, store training data, and compare the evaluation metrics in the same dashboard. You can use Keras to build and train your deep neural network models, and then package them as pipeline components that can be reused and combined with other components. You can also use Kubeflow Pipelines SDK to define and submit your pipelines programmatically, and use Kubeflow Pipelines UI to monitor and manage your experiments. Therefore, creating an experiment in Kubeflow Pipelines to organize multiple runs is the best option for this use case.

Kubeflow Pipelines documentation

Experiment | Kubeflow

Cloud Data Fusion is a fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. It provides a graphical interface to increase time efficiency and reduce complexity, and allows users to easily create and explore data pipelines using a code-free, point and click visual interface. Cloud Data Fusion also supports a broad range of data sources and formats, including on-premises data marts, and ensures data quality and security by using built-in transformation capabilities and Cloud Data Loss Prevention. Cloud Data Fusion lowers the total cost of ownership by handling performance, scalability, availability, security, and compliance needs automatically.Reference:

Cloud Data Fusion documentation

Cloud Data Fusion overview

Sentiment analysis is the process of identifying and extracting the emotions, opinions, and attitudes expressed in a text or speech. Sentiment analysis can help businesses understand their customers' feedback, satisfaction, and preferences. There are different approaches to building a sentiment analysis tool, depending on the input data and the output format. Some of the common approaches are:

Extracting sentiment directly from the voice recordings: This approach involves using acoustic features, such as pitch, intensity, and prosody, to infer the sentiment of the speaker. This approach can capture the nuances and subtleties of the vocal expression, but it also requires a large and diverse dataset of labeled voice recordings, which may not be easily available or accessible. Moreover, this approach may not account for the semantic and contextual information of the speech, which can also affect the sentiment.

Converting the speech to text and building a model based on the words: This approach involves using automatic speech recognition (ASR) to transcribe the voice recordings into text, and then using lexical features, such as word frequency, polarity, and valence, to infer the sentiment of the text. This approach can leverage the existing text-based sentiment analysis models and tools, but it also introduces some challenges, such as the accuracy and reliability of the ASR system, the ambiguity and variability of the natural language, and the loss of the acoustic information of the speech.

Converting the speech to text and extracting sentiments based on the sentences: This approach involves using ASR to transcribe the voice recordings into text, and then using syntactic and semantic features, such as sentence structure, word order, and meaning, to infer the sentiment of the text. This approach can capture the higher-level and complex aspects of the natural language, such as negation, sarcasm, and irony, which can affect the sentiment. However, this approach also requires more sophisticated and advanced natural language processing techniques, such as parsing, dependency analysis, and semantic role labeling, which may not be readily available or easy to implement.

Converting the speech to text and extracting sentiment using syntactical analysis: This approach involves using ASR to transcribe the voice recordings into text, and then using syntactical analysis, such as part-of-speech tagging, phrase chunking, and constituency parsing, to infer the sentiment of the text. This approach can identify the grammatical and structural elements of the natural language, such as nouns, verbs, adjectives, and clauses, which can indicate the sentiment. However, this approach may not account for the pragmatic and contextual information of the speech, such as the speaker's intention, tone, and situation, which can also influence the sentiment.

For the use case of building a sentiment analysis tool that predicts customer sentiment from recorded phone conversations, the best approach is to convert the speech to text and extract sentiments based on the sentences. This approach can balance the trade-offs between the accuracy, complexity, and feasibility of the sentiment analysis tool, while ensuring that the gender, age, and cultural differences of the customers who called the contact center do not impact any stage of the model development pipeline and results. This approach can also handle different types and levels of sentiment, such as polarity (positive, negative, or neutral), intensity (strong or weak), and emotion (anger, joy, sadness, etc.). Therefore, converting the speech to text and extracting sentiments based on the sentences is the best approach for this use case.

A predict request is a way to send data to a trained model and get predictions in return. A predict request can be written in different formats, such as JSON, protobuf, or gRPC, depending on the service and the platform that are used to host and serve the model. A predict request usually contains the following information:

The signature name: This is the name of the signature that defines the inputs and outputs of the model. A signature is a way to specify the expected format, type, and shape of the data that the model can accept and produce. A signature can be specified when exporting or saving the model, or it can be automatically inferred by the service or the platform. A model can have multiple signatures, but only one can be used for each predict request.

The instances: This is the data that is sent to the model for prediction. The instances can be a single instance or a batch of instances, depending on the size and shape of the data. The instances should match the input specification of the signature, such as the number, name, and type of the input tensors.

For the use case of training a text classification model, the correct way to write the predict request is D. data json.dumps({''signature_name'': ''serving_default'', ''instances'': [['a', 'b'], ['c', 'd'], ['e', 'f']]})

This option involves writing the predict request in JSON format, which is a common and convenient format for sending and receiving data over the web. JSON stands for JavaScript Object Notation, and it is a way to represent data as a collection of name-value pairs or an ordered list of values. JSON can be easily converted to and from Python objects using the json module.

This option also involves using the signature name ''serving_default'', which is the default signature name that is assigned to the model when it is saved or exported without specifying a custom signature name. The serving_default signature defines the input and output tensors of the model based on the SignatureDef that is shown in the image. According to the SignatureDef, the model expects an input tensor called ''text'' that has a shape of (-1, 2) and a type of DT_STRING, and produces an output tensor called ''softmax'' that has a shape of (-1, 2) and a type of DT_FLOAT. The -1 in the shape indicates that the dimension can vary depending on the number of instances, and the 2 indicates that the dimension is fixed at 2. The DT_STRING and DT_FLOAT indicate that the data type is string and float, respectively.

This option also involves sending a batch of three instances to the model for prediction. Each instance is a list of two strings, such as ['a', 'b'], ['c', 'd'], or ['e', 'f']. These instances match the input specification of the signature, as they have a shape of (3, 2) and a type of string. The model will process these instances and produce a batch of three predictions, each with a softmax output that has a shape of (1, 2) and a type of float. The softmax output is a probability distribution over the two possible classes that the model can predict, such as positive or negative sentiment.

Therefore, writing the predict request as data json.dumps({''signature_name'': ''serving_default'', ''instances'': [['a', 'b'], ['c', 'd'], ['e', 'f']]}) is the correct and valid way to send data to the text classification model and get predictions in return.

[json --- JSON encoder and decoder]