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NEW QUESTION # 129
A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.
Which model will meet the business requirement?
- A. K-means
- B. Linear regression
- C. Principal component analysis (PCA)
- D. Logistic regression
Answer: B
NEW QUESTION # 130
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
- A. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucket. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
- B. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
- C. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
- D. Use Cloud Scheduler to schedule jobs at a regular interval. For the first step of the job. check the timestamp of objects in your Cloud Storage bucket If there are no new files since the last run, abort the job.
Answer: A
Explanation:
https://cloud.google.com/architecture/architecture-for-mlops-using-tfx-kubeflow-pipelines-and-cloud-build#triggering-and-scheduling-kubeflow-pipelines
NEW QUESTION # 131
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?
- A. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
- B. The learning rate should be increased because the optimization process was trapped at a local minimum.
- C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
- D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: A
Explanation:
Explanation/Reference: https://www.tensorflow.org/tutorials/keras/overfit_and_underfit
NEW QUESTION # 132
This graph shows the training and validation loss against the epochs for a neural network.
The network being trained is as follows:
* Two dense layers, one output neuron
* 100 neurons in each layer
* 100 epochs
* Random initialization of weights
Which technique can be used to improve model performance in terms of accuracy in the validation set?
- A. Random initialization of weights with appropriate seed
- B. Increasing the number of epochs
- C. Early stopping
- D. Adding another layer with the 100 neurons
Answer: B
NEW QUESTION # 133
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?
- A. Use Al Platform Notebooks to execute the experiments. Collect the results in a shared Google Sheets file, and query the results using the Google Sheets API
- B. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
- C. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
- D. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
Answer: D
Explanation:
https://codelabs.developers.google.com/codelabs/cloud-kubeflow-pipelines-gis Kubeflow Pipelines (KFP) helps solve these issues by providing a way to deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility. Cloud AI Pipelines makes it easy to set up a KFP installation.
https://www.kubeflow.org/docs/components/pipelines/introduction/#what-is-kubeflow-pipelines
"Kubeflow Pipelines supports the export of scalar metrics. You can write a list of metrics to a local file to describe the performance of the model. The pipeline agent uploads the local file as your run-time metrics. You can view the uploaded metrics as a visualization in the Runs page for a particular experiment in the Kubeflow Pipelines UI." https://www.kubeflow.org/docs/components/pipelines/sdk/pipelines-metrics/
NEW QUESTION # 134
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?
- A. Convert the speech to text and build a model based on the words
- B. Convert the speech to text and extract sentiments based on the sentences
- C. Extract sentiment directly from the voice recordings
- D. Convert the speech to text and extract sentiment using syntactical analysis
Answer: B
NEW QUESTION # 135
You developed a custom model by using Vertex Al to forecast the sales of your company s products based on historical transactional data You anticipate changes in the feature distributions and the correlations between the features in the near future You also expect to receive a large volume of prediction requests You plan to use Vertex Al Model Monitoring for drift detection and you want to minimize the cost. What should you do?
- A. Use the features for monitoring Set a prediction-sampling-rare value that is closer to 1 than 0.
- B. Use the features and the feature attributions for monitoring Set a prediction-sampling-rate value that is closer to 0 than 1.
- C. Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.
- D. Use the features for monitoring Set a monitoring- frequency value that is higher than the default.
Answer: B
Explanation:
The best option for using Vertex AI Model Monitoring for drift detection and minimizing the cost is to use the features and the feature attributions for monitoring, and set a prediction-sampling-rate value that is closer to 0 than 1. This option allows you to leverage the power and flexibility of Google Cloud to detect feature drift in the input predict requests for custom models, and reduce the storage and computation costs of the model monitoring job. Vertex AI Model Monitoring is a service that can track and compare the results of multiple machine learning runs. Vertex AI Model Monitoring can monitor the model's prediction input data for feature skew and drift. Feature drift occurs when the feature data distribution in production changes over time. If the original training data is not available, you can enable drift detection to monitor your models for feature drift.
Vertex AI Model Monitoring uses TensorFlow Data Validation (TFDV) to calculate the distributions and distance scores for each feature, and compares them with a baseline distribution. The baseline distribution is the statistical distribution of the feature's values in the training data. If the training data is not available, the baseline distribution is calculated from the first 1000 prediction requests that the model receives. If the distance score for a feature exceeds an alerting threshold that you set, Vertex AI Model Monitoring sends you an email alert. However, if you use a custom model, you can also enable feature attribution monitoring, which can provide more insights into the feature drift. Feature attribution monitoring analyzes the feature attributions, which are the contributions of each feature to the prediction output. Feature attribution monitoring can help you identify the features that have the most impact on the model performance, and the features that have the most significant drift over time. Feature attribution monitoring can also help you understand the relationship between the features and the prediction output, and the correlation between the features1. The prediction-sampling-rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a lower prediction-sampling-rate can reduce the storage and computation costs of the model monitoring job, but also the quality and validity of the data. Using a lower prediction-sampling-rate can introduce sampling bias and noise into the data, and make the model monitoring job miss some important features or patterns of the data. However, using a higher prediction-sampling-rate can increase the storage and computation costs of the model monitoring job, and also the amount of data that needs to be processed and analyzed. Therefore, there is a trade-off between the prediction-sampling-rate and the cost and accuracy of the model monitoring job, and the optimal prediction-sampling-rate depends on the business objective and the data characteristics2. By using the features and the feature attributions for monitoring, and setting a prediction-sampling-rate value that is closer to 0 than 1, you can use Vertex AI Model Monitoring for drift detection and minimize the cost.
The other options are not as good as option D, for the following reasons:
* Option A: Using the features for monitoring and setting a monitoring-frequency value that is higher than the default would not enable feature attribution monitoring, and could increase the cost of the model monitoring job. The monitoring-frequency is a parameter that determines how often the model monitoring job analyzes the logged prediction requests and calculates the distributions and distance scores for each feature. Using a higher monitoring-frequency can increase the frequency and timeliness of the model monitoring job, but also the computation costs of the model monitoring job. Moreover, using the features for monitoring would not enable feature attribution monitoring, which can provide more insights into the feature drift and the model performance1.
* Option B: Using the features for monitoring and setting a prediction-sampling-rate value that is closer to
1 than 0 would not enable feature attribution monitoring, and could increase the cost of the model monitoring job. The prediction-sampling-rate is a parameter that determines the percentage of prediction requests that are logged and analyzed by the model monitoring job. Using a higher prediction-sampling-rate can increase the quality and validity of the data, but also the storage and computation costs of the model monitoring job. Moreover, using the features for monitoring would not enable feature attribution monitoring, which can provide more insights into the feature drift and the model performance12.
* Option C: Using the features and the feature attributions for monitoring and setting a monitoring-frequency value that is lower than the default would enable feature attribution monitoring, but could reduce the frequency and timeliness of the model monitoring job. The monitoring-frequency is a parameter that determines how often the model monitoring job analyzes the logged prediction requests and calculates the distributions and distance scores for each feature. Using a lower monitoring-frequency can reduce the computation costs of the model monitoring job, but also the frequency and timeliness of the model monitoring job. This can make the model monitoring job less responsive and effective in detecting and alerting the feature drift1.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 4: Evaluation
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.3 Monitoring ML models in production
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.3: Monitoring ML Models
* Using Model Monitoring
* Understanding the score threshold slider
NEW QUESTION # 136
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora:
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?
- A. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- B. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
- C. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
- D. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
Answer: C
NEW QUESTION # 137
Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?
- A. Convert the model to a Keras model, and run a Keras Tuner job.
- B. Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.
- C. Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.
- D. Run a hyperparameter tuning job on AI Platform using custom containers.
Answer: C
NEW QUESTION # 138
You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?
- A. Develop an image segmentation ML model to locate the boundaries of the rust spots.
- B. Develop an image classification ML model to predict the presence of the disease.
- C. Develop a template matching algorithm using traditional computer vision libraries.
- D. Create an object detection model that can localize the rust spots.
Answer: A
NEW QUESTION # 139
You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor's batch number serial number dimensions, and weight You need to configure model training and serving while maximizing model accuracy. What should you do?
- A. Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model.
Deploy the model and configure Pub/Sub to publish a message when an image is categorized into the failing class. - B. Convert the images into an embedding representation Import this data into BigQuery, and train a BigQuery. ML K-means clustenng model with two clusters Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing cluster.
- C. Import the tabular data into BigQuery use Vertex Al Data Labeling Service to label the data and train an AutoML tabular classification model Deploy the model and configure Pub/Sub to publish a message when a semiconductor's data is categorized into the failing class.
- D. Use Vertex Al Data Labeling Service to label the images and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.
Answer: A
Explanation:
Vertex AI is a unified platform for building and managing machine learning solutions on Google Cloud. It provides various services and tools for different stages of the machine learning lifecycle, such as data preparation, model training, deployment, monitoring, and experimentation. Vertex AI Data Labeling Service is a service that allows you to create and manage human-labeled datasets for machine learning. You can use Vertex AI Data Labeling Service to label the images of semiconductors with binary labels, such as "pass" or
"fail", based on the quality criteria. You can also use Vertex AI AutoML Image Classification, which is a service that allows you to create and train custom image classification models without writing any code. You can use Vertex AI AutoML Image Classification to train an image classification model on the labeled images of semiconductors, and optimize the model for accuracy. You can also use Vertex AI to deploy the model to an endpoint, which is a service that allows you to serve online predictions from your model. You can configure Pub/Sub, which is a service that allows you to publish and subscribe to messages, to publish a message when an image is categorized into the failing class by the model. You can use the message to trigger an action, such as alerting the quality control team or stopping the production line. This solution can help you create a real-time application that automates the quality control process of semiconductors, and maximizes the model accuracy. References: The answer can be verified from official Google Clouddocumentation and resources related to Vertex AI, Vertex AI Data Labeling Service, Vertex AI AutoML Image Classification, and Pub/Sub.
* Vertex AI | Google Cloud
* Vertex AI Data Labeling Service | Google Cloud
* Vertex AI AutoML Image Classification | Google Cloud
* Pub/Sub | Google Cloud
NEW QUESTION # 140
You developed a custom model by using Vertex Al to predict your application's user churn rate You are using Vertex Al Model Monitoring for skew detection The training data stored in BigQuery contains two sets of features - demographic and behavioral You later discover that two separate models trained on each set perform better than the original model You need to configure a new model mentioning pipeline that splits traffic among the two models You want to use the same prediction-sampling-rate and monitoring-frequency for each model You also want to minimize management effort What should you do?
- A. Separate the training dataset into two tables based on demographic and behavioral features Deploy the models to two separate endpoints, and submit two Vertex Al Model Monitoring jobs
- B. Keep the training dataset as is Deploy the models to two separate endpoints and submit two Vertex Al Model Monitoring jobs with appropriately selected feature-thresholds parameters
- C. Keep the training dataset as is Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and feature selections
- D. Separate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint and submit a Vertex Al Model Monitoring job with a monitoring-config-from parameter that accounts for the model IDs and training datasets
Answer: C
NEW QUESTION # 141
You are training a custom language model for your company using a large dataset. You plan to use the ReductionServer strategy on Vertex Al. You need to configure the worker pools of the distributed training job.
What should you do?
- A. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs Configure the third worker pool to have GPUs: and use the reduction server container image.
- B. Configure the machines of the first two pools to have TPUs. and to use a container image where your training code runs Configure the third pool to have TPUs: and use the reductionserver container image.
- C. Configure the machines of the first two worker pools to have TPUs and to use a container image where your training code runs Configure the third worker pool without accelerators, and use the reductionserver container image without accelerators and choose a machine type that prioritizes bandwidth.
- D. Configure the machines of the first two worker pools to have GPUs and to use a container image where your training code runs. Configure the third worker pool to use the reductionserver container image without accelerators, and choose a machine type that prioritizes bandwidth.
Answer: D
Explanation:
According to the web search results, Reduction Server is a faster GPU all-reduce algorithm developed at Google that uses a dedicated set of reducers to aggregate gradients from workers12. Reducers are lightweight CPU VM instances that are significantly cheaper than GPU VMs2. Therefore, the third worker pool should not have any accelerators, and should use a machine type that has high network bandwidth to optimize the communication between workers and reducers2. TPUs are not supported by Reduction Server, so the first two worker pools should have GPUs and use a container image that contains the training code12. The reduction-server container image is provided by Google and should be used for the third worker pool2.
NEW QUESTION # 142
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": [fab', 'be1, 'cd']]})
- B. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})
- C. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})
- D. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
Answer: B
NEW QUESTION # 143
You work for a delivery company. You need to design a system that stores and manages features such as parcels delivered and truck locations over time. The system must retrieve the features with low latency and feed those features into a model for online prediction. The data science team will retrieve historical data at a specific point in time for model training. You want to store the features with minimal effort. What should you do?
- A. Store features in Vertex Al Feature Store.
- B. Store features in BigQuery timestamp partitioned tables, and use the BigQuery Storage Read API to serve the features.
- C. Store features in Bigtable as key/value data.
- D. Store features as a Vertex Al dataset and use those features to tram the models hosted in Vertex Al endpoints.
Answer: A
Explanation:
Vertex AI Feature Store is a service that allows you to store and manage your ML features on Google Cloud.
You can use Vertex AI Feature Store to store features such as parcels delivered and truck locations over time, and retrieve them with low latency for online prediction. Online prediction is a type of prediction that provides low-latency responses to individual or small batches of input data. You can also use Vertex AI Feature Store to retrieve historical data at a specific point in time for model training. Model training is a process of learning the parameters of a ML model from data. By using Vertex AI Feature Store, you can store the features with minimal effort, and avoid the complexity of managing your own data storage and serving system. References:
* Vertex AI Feature Store documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate
NEW QUESTION # 144
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?
- A. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
- B. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR
- C. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR
- D. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR
Answer: A
Explanation:
Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
Reference: https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/
NEW QUESTION # 145
You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible. What should you do?
- A. Load the model directly into the Dataflow job as a dependency, and use it for prediction.
- B. Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
- C. Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.
- D. Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
Answer: A
Explanation:
The best option for creating a Dataflow pipeline for real-time anomaly detection is to load the model directly into the Dataflow job as a dependency, and use it for prediction. This option has the following advantages:
* It minimizes the serving latency, as the model prediction logic is executed within the same Dataflow pipeline that ingests and processes the data. There is no need to invoke external services or containers, which can introduce network overhead and latency.
* It simplifies the deployment and management of the model, as the model is packaged with the Dataflow job and does not require a separate service or container. The model can be updated by redeploying the Dataflow job with a new model version.
* It leverages the scalability and reliability of Dataflow, as the model prediction logic can scale up or down with the data volume and handle failures and retries automatically.
The other options are less optimal for the following reasons:
* Option A: Containerizing the model prediction logic in Cloud Run, which is invoked by Dataflow, introduces additional latency and complexity. Cloud Run is a serverless platform that runs stateless containers, which means that the model prediction logic needs to be initialized and loaded every time a request is made. This can increase the cold start latency and reduce the throughput. Moreover, Cloud Run has a limit on the number of concurrent requests per container, which can affect the scalability of the model prediction logic. Additionally, this option requires managing two separate services: the Dataflow pipeline and the Cloud Run container.
* Option C: Deploying the model to a Vertex AI endpoint, and invoking this endpoint in the Dataflow job, also introduces additional latency and complexity. Vertex AI is a managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. However, invoking a Vertex AI endpoint from a Dataflow job requires making an HTTP request, which can incur network overhead and latency. Moreover, this option requires managing two separate services: the Dataflow pipeline and the Vertex AI endpoint.
* Option D: Deploying the model in a TFServing container on Google Kubernetes Engine, and invoking it in the Dataflow job, also introduces additional latency and complexity. TFServing is a high-performance serving system for TensorFlow models, which can handle multiple versions and variants of a model.
However, invoking a TFServing container from a Dataflow job requires making a gRPC or REST request, which can incur network overhead and latency. Moreover, this option requires managing two separate services: the Dataflow pipeline and the Google Kubernetes Engine cluster.
References:
* [Dataflow documentation]
* [TensorFlow documentation]
* [Cloud Run documentation]
* [Vertex AI documentation]
* [TFServing documentation]
NEW QUESTION # 146
You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line Although your model is performing well some images in your holdout set are consistently mislabeled with high confidence You want to use Vertex Al to understand your model's results What should you do?
- A.
- B.
- C.
- D.
Answer: A
Explanation:
Vertex Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models, natively integrated with a number of Google's products and services1. With Vertex Explainable AI, you can generate feature-based explanations that show how much each input feature contributed to the model's prediction2. This can help you debug and improve your model performance, and build confidence in your model's behavior. Feature-based explanations are supported for custom image classification models deployed on Vertex AI Prediction3. References:
* Explainable AI | Google Cloud
* Introduction to Vertex Explainable AI | Vertex AI | Google Cloud
* Supported model types for feature-based explanations | Vertex AI | Google Cloud
NEW QUESTION # 147
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?
- A. Differential privacy federated learning, and explainability
- B. Traceability, reproducibility, and explainability
- C. Redaction, reproducibility, and explainability
- D. Federated learning, reproducibility, and explainability
Answer: B
Explanation:
https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
https://medium.com/artefact-engineering-and-data-science/including-ethics-best-practices-in-your-data-science-project-from-day-one-c15b26c2bf99
NEW QUESTION # 148
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