[Sep 20, 2024] Latest Professional-Machine-Learning-Engineer PDF Dumps & Real Tests Free Updated Today [Q98-Q122]

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[Sep 20, 2024] Latest Professional-Machine-Learning-Engineer PDF Dumps & Real Tests Free Updated Today

Professional-Machine-Learning-Engineer Dumps With 100% Verified Q&As – Pass Guarantee or Full Refund

Google Professional Machine Learning Engineer is a certification exam offered by Google Cloud. It is designed to test the skills and knowledge required to design, build, and deploy machine learning models on Google Cloud Platform. Professional-Machine-Learning-Engineer exam is intended for individuals who have experience in machine learning and wish to demonstrate their proficiency in designing and implementing machine learning models using Google Cloud technologies.

How to Prepare For Professional Machine Learning Engineer – Google

Preparation Guide for Professional Machine Learning Engineer – Google

Introduction for Professional Machine Learning Engineer – Google

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.

The Professional Machine Learning Engineer exam assesses your ability to:

Frame ML problems
Develop ML models
Monitor, optimize, and maintain ML solutions

We prepare Google Professional-Machine-Learning-Engineer practice exams and Google Professional-Machine-Learning-Engineer practice exams to prepare you for all these requirements.

Google Professional Machine Learning Engineer certification exam is a great way for professionals to showcase their expertise in designing and developing machine learning models on Google Cloud Platform. Google Professional Machine Learning Engineer certification exam covers various topics related to machine learning, and passing the exam demonstrates the individual’s ability to use Google Cloud Platform tools and services to create scalable and efficient machine learning models. Google Professional Machine Learning Engineer certification exam is a credible and recognized way for professionals to demonstrate their skills and knowledge in the field of machine learning.

NEW QUESTION 98
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?

Create alerts to monitor for skew, and retrain the model.

Perform feature selection on the model, and retrain the model with fewer features

Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service

Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

NEW QUESTION 99
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

Use the Al Platform custom containers feature to receive training jobs using any framework

Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob

Create a library of VM images on Compute Engine; and publish these images on a centralized repository

Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

A cloud-based backend system is a system that runs on a cloud platform and provides services or resources to other applications or users. A cloud-based backend system can be used to submit training jobs, which are tasks that involve training a machine learning model on a given dataset using a specific framework and configuration1 However, a cloud-based backend system can also have some drawbacks, such as:
* High maintenance: A cloud-based backend system may require a lot of administration and management, such as provisioning, scaling, monitoring, and troubleshooting the cloud resources and services. This can be time-consuming and costly, and may distract from the core business objectives2
* Low flexibility: A cloud-based backend system may not support all the frameworks and libraries that the data scientists need to use for their training jobs. This can limit the choices and capabilities of the data scientists, and affect the quality and performance of their models3
* Poor integration: A cloud-based backend system may not integrate well with other cloud services or
* tools that the data scientists need to use for their machine learning workflows, such as data processing, model deployment, or model monitoring. This can create compatibility and interoperability issues, and reduce the efficiency and productivity of the data scientists.
Therefore, it may be better to use a managed service instead of a cloud-based backend system to submit training jobs. A managed service is a service that is provided and operated by a third-party provider, and offers various benefits, such as:
* Low maintenance: A managed service handles the administration and management of the cloud resources and services, and abstracts away the complexity and details of the underlying infrastructure. This can save time and money, and allow the data scientists to focus on their core tasks2
* High flexibility: A managed service can support multiple frameworks and libraries that the data scientists need to use for their training jobs, and allow them to customize and configure their training environments and parameters. This can enhance the choices and capabilities of the data scientists, and improve the quality and performance of their models3
* Easy integration: A managed service can integrate seamlessly with other cloud services or tools that the data scientists need to use for their machine learning workflows, and provide a unified and consistent interface and experience. This can solve the compatibility and interoperability issues, and increase the efficiency and productivity of the data scientists.
One of the best options for using a managed service to submit training jobs is to use the AI Platform custom containers feature to receive training jobs using any framework. AI Platform is a Google Cloud service that provides a platform for building, deploying, and managing machine learning models. AI Platform supports various machine learning frameworks, such as TensorFlow, PyTorch, scikit-learn, and XGBoost, and provides various features, such as hyperparameter tuning, distributed training, online prediction, and model monitoring.
The AI Platform custom containers feature allows the data scientists to use any framework or library that they want for their training jobs, and package their training application and dependencies as a Docker container image. The data scientists can then submit their training jobs to AI Platform, and specify the container image and the training parameters. AI Platform will run the training jobs on the cloud infrastructure, and handle the scaling, logging, and monitoring of the training jobs. The data scientists can also use the AI Platform features to optimize, deploy, and manage their models.
The other options are not as suitable or feasible. Configuring Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob is not ideal, as Kubeflow is mainly designed for TensorFlow-based training jobs, and does not support other frameworks or libraries. Creating a library of VM images on Compute Engine and publishing these images on a centralized repository is not optimal, as Compute Engine is a low-level service that requires a lot of administration and management, and does not provide the features and integrations of AI Platform. Setting up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure is not relevant, as Slurm is a tool for managing and scheduling jobs on a cluster of nodes, and does not provide a managed service for training jobs.
References: 1: Cloud computing 2: Managed services 3: Machine learning frameworks : [Machine learning workflow] : [AI Platform overview] : [Custom containers for training]

NEW QUESTION 100
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?

Kubeflow Pipelines and App Engine

Kubeflow Pipelines and Al Platform Prediction

Cloud Composer, BigQuery ML , and Al Platform Prediction

Cloud Composer, Al Platform Training with custom containers , and App Engine

NEW QUESTION 101
Your team is building a convolutional neural network (CNN)-based architecture from scratch. The preliminary experiments running on your on-premises CPU-only infrastructure were encouraging, but have slow convergence. You have been asked to speed up model training to reduce time-to-market. You want to experiment with virtual machines (VMs) on Google Cloud to leverage more powerful hardware. Your code does not include any manual device placement and has not been wrapped in Estimator model-level abstraction. Which environment should you train your model on?

AVM on Compute Engine and 1 TPU with all dependencies installed manually.

AVM on Compute Engine and 8 GPUs with all dependencies installed manually.

A Deep Learning VM with an n1-standard-2 machine and 1 GPU with all libraries pre-installed.

A Deep Learning VM with more powerful CPU e2-highcpu-16 machines with all libraries pre-installed.

NEW QUESTION 102
You are developing a model to predict whether a failure will occur in a critical machine part. You have a dataset consisting of a multivariate time series and labels indicating whether the machine part failed You recently started experimenting with a few different preprocessing and modeling approaches in a Vertex Al Workbench notebook. You want to log data and track artifacts from each run. How should you set up your experiments?

The option A is the most suitable solution for logging data and tracking artifacts from each run of a model development experiment in a Vertex AI Workbench notebook. Vertex AI Workbench is a service that allows you to create and run interactive notebooks on Google Cloud. You can use Vertex AI Workbench to experiment with different preprocessing and modeling approaches for your time series prediction problem.
You can also use the Vertex AI TensorBoard instance and the Vertex AI SDK to create an experiment and associate the TensorBoard instance. TensorBoard is a tool that allows you to visualize and monitor the metrics and artifacts of your ML experiments. You can use the Vertex AI SDK to create an experiment object, which is a logical grouping of runs that share a common objective. You can also use the Vertex AI SDK to associate the experiment object with a TensorBoard instance, which is a managed service that hosts a TensorBoard web app. By using the Vertex AI TensorBoard instance and the Vertex AI SDK, you can easily set up and manage your experiments, and access the TensorBoard web app from the Vertex AI console. You can also use the log_time_series_metrics function and the log_metrics function to log data and track artifacts from each run.
The log_time_series_metrics function is a function that allows you to log the time series data, such as the multivariate time series and the labels, to the TensorBoard instance. The log_metrics function is a function that allows you to log the scalar metrics, such as the loss values, to the TensorBoard instance. By using these functions, you can record the data and artifacts from each run of your experiment, and compare them in the TensorBoard web app. You can also use the TensorBoard web app to visualize the data and artifacts, such as the time series plots, the scalar charts, the histograms, and the distributions. By using the Vertex AI TensorBoard instance, the Vertex AI SDK, and the log functions, you can log data and track artifacts from each run of your experiment in a Vertex AI Workbench notebook. References:
* Vertex AI Workbench documentation
* Vertex AI TensorBoard documentation
* Vertex AI SDK documentation
* log_time_series_metrics function documentation
* log_metrics function documentation
* [Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate]

NEW QUESTION 103
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model’s performance? (Choose One Correct Answer)

Average time players wait before being assigned to a team

Precision and recall of assigning players to teams based on their predicted versus actual ability

User engagement as measured by the number of battles played daily per user

Rate of return as measured by additional revenue generated minus the cost of developing a new model

NEW QUESTION 104
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?

Determine whether there is a meaningful correlation between the sensitive features and the other features Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features

Train a BigQuery ML boosted trees classification model with all features Use the ml. global explain method to calculate the global attribution values for each feature of the model If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature

Train a BigQuery ML boosted trees classification model with all features Use the ml. exflain_predict method to calculate the attribution values for each feature for each customer in a test set If for any individual customer the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.

Define a fairness metric that is represented by accuracy across the sensitive features Train a BigQuery ML boosted trees classification model with all features Use the trained model to make predictions on a test set Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.

* A fairness metric is a way to measure how well a machine learning model treats different groups of customers, such as by sex or age. A common fairness metric is accuracy, which is the proportion of correct predictions among all predictions. Accuracy across the sensitive features means calculating the accuracy for each group separately, and then comparing them. For example, if the model has 90% accuracy for male customers and 80% accuracy for female customers, there is a 10% accuracy gap that indicates potential bias against female customers.
* To investigate and mitigate potential bias, it is important to define a fairness metric and evaluate it on a test set. A test set is a subset of the data that is not used for training the model, but only for evaluating its performance. By joining the test set predictions with the sensitive features, you can calculate the fairness metric and see if it meets your requirements. For example, you may require that the accuracy gap between any two groups is less than 5%. If the fairness metric does not meet your requirements, you may need to adjust the model or the data to reduce bias.
* Option A is not the best answer because excluding the sensitive features and any meaningfully correlated features may not eliminate bias. For example, if salary is correlated with sex, and salary is also a predictive feature for the target variable, excluding both features may reduce the model accuracy and still leave some residual bias. Moreover, excluding features based on correlation may not capture the complex interactions and dependencies among the features that may affect bias.
* Option B is not the best answer because using the global attribution values for each feature of the model may not reflect the individual-level impact of the features on the predictions. Global attribution values are calculated by averaging the attribution values across all the data points, and they indicate how important each feature is for the overall model performance. However, they do not show how each
* feature affects each customer’s prediction, which may vary depending on the values of the other features. For example, sex may have a low global attribution value, but it may have a high impact on some customers’ predictions, especially if it interacts with other features such as salary or age.
* Option C is not the best answer because discarding the model and training the model again without a feature based on a single customer’s attribution value may not be a robust or scalable way to mitigate bias. Attribution values are calculated by measuring how much each feature contributes to the prediction for a given data point, and they indicate how sensitive the prediction is to the feature value. However, they do not show how the feature affects the overall fairness metric or the model accuracy. For example, sex may have a high attribution value for a customer, but it may not affect the accuracy gap between the groups. Moreover, discarding and retraining the model based on a single customer’s attribution value may not be feasible if there are many customers with high attribution values for different features.

NEW QUESTION 105
You work for a startup that has multiple data science workloads. Your compute infrastructure is currently on-premises. and the data science workloads are native to PySpark Your team plans to migrate their data science workloads to Google Cloud You need to build a proof of concept to migrate one data science job to Google Cloud You want to propose a migration process that requires minimal cost and effort. What should you do first?

Create a n2-standard-4 VM instance and install Java, Scala and Apache Spark dependencies on it.

Create a Google Kubemetes Engine cluster with a basic node pool configuration install Java Scala, and Apache Spark dependencies on it.

Create a Standard (1 master. 3 workers) Dataproc cluster, and run a Vertex Al Workbench notebook instance on it.

Create a Vertex Al Workbench notebook with instance type n2-standard-4.

NEW QUESTION 106
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time. User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model’s performance?

Average time players wait before being assigned to a team

Precision and recall of assigning players to teams based on their predicted versus actual ability

User engagement as measured by the number of battles played daily per user

Rate of return as measured by additional revenue generated minus the cost of developing a new model

NEW QUESTION 107
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.

Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.

Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.

Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

NEW QUESTION 108
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online prediction

Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

Stream incoming prediction request data into Cloud Spanner
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.

Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue

NEW QUESTION 109
You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

This is a good result because the accuracy across both groups is greater than 80%.

This is not a good result because the model is performing worse than predicting that people will always renew their subscription. This option has the following reasons:
* It indicates that the model is not learning from the data, but rather memorizing the majority class. Since
90% of the individuals renew their subscription every year, the model can achieve a 90% accuracy by simply predicting that everyone will renew their subscription, without considering the features or the patterns in the data. However, the model’s accuracy for predicting those who renew their subscription is only 82%, which is lower than the baseline accuracy of 90%. This suggests that the model is overfitting to the minority class (those who cancel their subscription), and underfitting to the majority class (those who renew their subscription).
* It implies that the model is not useful for the business problem, as it cannot identify the customers who are at risk of churning. The goal of predicting whether customers will cancel their annual subscription is to prevent customer churn and increase customer retention. However, the model’s accuracy for predicting those who cancel their subscription is 99%, which is too high and unrealistic, as it means that the model can almost perfectly identify the customers who will churn, without any false positives or false negatives. This may indicate that the model is cheating or exploiting some leakage in the data, such as a feature that reveals the outcome of the prediction. Moreover, the model’s accuracy for predicting those who renew their subscription is 82%, which is too low and unreliable, as it means that the model
* can miss many customers who will churn, and falsely label them as renewing customers. This can lead to losing customers and revenue, and failing to take proactive actions to retain them.
References:
* How to Evaluate Machine Learning Models: Classification Metrics | Machine Learning Mastery
* Imbalanced Classification: Predicting Subscription Churn | Machine Learning Mastery

NEW QUESTION 110
You have created a Vertex Al pipeline that automates custom model training You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Query the table to compare different executions of the pipeline Connect BigQuery to Looker Studio to visualize metrics.

Add a component to the Vertex Al pipeline that logs metrics to a BigQuery table Load the table into a pandas DataFrame to compare different executions of the pipeline Use Matplotlib to visualize metrics.

Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Use Vertex Al Experiments to compare different executions of the pipeline Use Vertex Al TensorBoard to visualize metrics.

Add a component to the Vertex Al pipeline that logs metrics to Vertex ML Metadata Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.

NEW QUESTION 111
You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

Set up Vertex Al Experiments to track metrics and parameters Configure Vertex Al TensorBoard for visualization.

Set up a Cloud Function to write and save metrics files to a Cloud Storage Bucket Configure a Google Cloud VM to host TensorBoard locally for visualization.

Set up a Vertex Al Workbench notebook instance Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.

Set up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

NEW QUESTION 112
A Data Engineer needs to build a model using a dataset containing customer credit card information How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?

Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.

Use an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.

Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC. Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.

Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.

NEW QUESTION 113
You are profiling the performance of your TensorFlow model training time and notice a performance issue caused by inefficiencies in the input data pipeline for a single 5 terabyte CSV file dataset on Cloud Storage. You need to optimize the input pipeline performance. Which action should you try first to increase the efficiency of your pipeline?

Preprocess the input CSV file into a TFRecord file.

Randomly select a 10 gigabyte subset of the data to train your model.

Split into multiple CSV files and use a parallel interleave transformation.

Set the reshuffle_each_iteration parameter to true in the tf.data.Dataset.shuffle method.

NEW QUESTION 114
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year. The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and
999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue? (Choose two.)

Add more deep trees to the random forest to enable the model to learn more features.

Include a copy of the samples in the test dataset in the training dataset.

Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

Change the cost function so that false negatives have a higher impact on the cost value than false positives.

Change the cost function so that false positives have a higher impact on the cost value than false negatives.

NEW QUESTION 115
Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction

1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2 Dispatch an appropriately sized shuttle and indicate the required stops on the map

1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance-based metric
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

NEW QUESTION 116
You are an ML engineer in the contact center of a large enterprise. You need to build a sentiment analysis tool that predicts customer sentiment from recorded phone conversations. You need to identify the best approach to building a model 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. What should you do?

Extract sentiment directly from the voice recordings

Convert the speech to text and build a model based on the words

Convert the speech to text and extract sentiments based on the sentences

Convert the speech to text and extract sentiment using syntactical analysis

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.

NEW QUESTION 117
You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

Store image files in Cloud Storage and access them directly.

Store image files in Cloud Storage and access them by using serialized records.

Store image files in Cloud Filestore, and access them by using serialized records.

Store image files in Cloud Filestore and access them directly by using an NFS mount point.

NEW QUESTION 118
You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users’ profile information and credit card transaction dat a. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?
Choose 2 answers

Increase the score threshold.

Decrease the score threshold.

Add more positive examples to the training set.

Add more negative examples to the training set.

Reduce the maximum number of node hours for training.

NEW QUESTION 119
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

NEW QUESTION 120
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

Export the model to BigQuery ML.

Deploy and version the model on Al Platform.

Use Dataflow with the SavedModel to read the data from BigQuery

Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

NEW QUESTION 121
You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.

Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.

Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.

Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance’s after the model is trained.

NEW QUESTION 122
Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?

Configure a Cloud Build trigger with the event set as “Pull Request”

Configure a Cloud Build trigger with the event set as “Push to a branch”

Configure a Cloud Function that builds the repository each time there is a code change.

Configure a Cloud Function that builds the repository each time a new branch is created.

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How to Prepare For Professional Machine Learning Engineer – Google

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