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Professional-Machine-Learning-Engineer Exam Dumps - Google Professional Machine Learning Engineer

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Question # 73

While monitoring your model training’s GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A.

Increase the CPU load

B.

Add caching to the pipeline

C.

Increase the network bandwidth

D.

Add parallel interleave to the pipeline

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Question # 74

You have successfully deployed to production a large and complex TensorFlow model trained on tabular data. You want to predict the lifetime value (LTV) field for each subscription stored in the BigQuery table named subscription. subscriptionPurchase in the project named my-fortune500-company-project.

You have organized all your training code, from preprocessing data from the BigQuery table up to deploying the validated model to the Vertex AI endpoint, into a TensorFlow Extended (TFX) pipeline. You want to prevent prediction drift, i.e., a situation when a feature data distribution in production changes significantly over time. What should you do?

A.

Implement continuous retraining of the model daily using Vertex AI Pipelines.

B.

Add a model monitoring job where 10% of incoming predictions are sampled 24 hours.

C.

Add a model monitoring job where 90% of incoming predictions are sampled 24 hours.

D.

Add a model monitoring job where 10% of incoming predictions are sampled every hour.

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Question # 75

Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API. You want to use the most efficient approach. What should you do?

A.

1 Upload the audio files to Cloud Storage

2. Call the speech: Iongrunningrecognize API endpoint to generate transcriptions

3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

B.

1 Upload the audio files to Cloud Storage

2 Call the speech: Iongrunningrecognize API endpoint to generate transcriptions.

3 Create a Cloud Function that calls the Natural Language API by using the analyzesentiment method

C.

1 Iterate over your local Tiles in Python

2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object and set the content to the audio file data

3. Call the speech: recognize API endpoint to generate transcriptions

4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions

D.

1 Iterate over your local files in Python

2 Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object, and set the content to the audio file data

3. Call the speech: lengrunningrecognize API endpoint to generate transcriptions

4 Call the Natural Language API by using the analyzesenriment method

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Question # 76

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

A.

Train local surrogate models to explain individual predictions.

B.

Configure sampled Shapley explanations on Vertex Explainable AI.

C.

Configure integrated gradients explanations on Vertex Explainable AI.

D.

Measure the effect of each feature as the weight of the feature multiplied by the feature value.

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Question # 77

You are a lead ML engineer at a retail company. You want to track and manage ML metadata in a centralized way so that your team can have reproducible experiments by generating artifacts. Which management solution should you recommend to your team?

A.

Store your tf.logging data in BigQuery.

B.

Manage all relational entities in the Hive Metastore.

C.

Store all ML metadata in Google Cloud’s operations suite.

D.

Manage your ML workflows with Vertex ML Metadata.

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Question # 78

You are investigating the root cause of a misclassification error made by one of your models. You used Vertex Al Pipelines to tram and deploy the model. The pipeline reads data from BigQuery. creates a copy of the data in Cloud Storage in TFRecord format trains the model in Vertex Al Training on that copy, and deploys the model to a Vertex Al endpoint. You have identified the specific version of that model that misclassified: and you need to recover the data this model was trained on. How should you find that copy of the data'?

A.

Use Vertex Al Feature Store Modify the pipeline to use the feature store; and ensure that all training data is stored in it Search the feature store for the data used for the training.

B.

Use the lineage feature of Vertex Al Metadata to find the model artifact Determine the version of the model and identify the step that creates the data copy, and search in the metadata for its location.

C.

Use the logging features in the Vertex Al endpoint to determine the timestamp of the models deployment Find the pipeline run at that timestamp Identify the step that creates the data copy; and search in the logs for its location.

D.

Find the job ID in Vertex Al Training corresponding to the training for the model Search in the logs of that job for the data used for the training.

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Question # 79

You are an ML engineer at a manufacturing company You are creating a classification model for a predictive maintenance use case You need to predict whether a crucial machine will fail in the next three days so that the repair crew has enough time to fix the machine before it breaks. Regular maintenance of the machine is relatively inexpensive, but a failure would be very costly You have trained several binary classifiers to predict whether the machine will fail. where a prediction of 1 means that the ML model predicts a failure.

You are now evaluating each model on an evaluation dataset. You want to choose a model that prioritizes detection while ensuring that more than 50% of the maintenance jobs triggered by your model address an imminent machine failure. Which model should you choose?

A.

The model with the highest area under the receiver operating characteristic curve (AUC ROC) and precision greater than 0 5

B.

The model with the lowest root mean squared error (RMSE) and recall greater than 0.5.

C.

The model with the highest recall where precision is greater than 0.5.

D.

The model with the highest precision where recall is greater than 0.5.

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Question # 80

You have been given a dataset with sales predictions based on your company’s marketing activities. The data is structured and stored in BigQuery, and has been carefully managed by a team of data analysts. You need to prepare a report providing insights into the predictive capabilities of the data. You were asked to run several ML models with different levels of sophistication, including simple models and multilayered neural networks. You only have a few hours to gather the results of your experiments. Which Google Cloud tools should you use to complete this task in the most efficient and self-serviced way?

A.

Use BigQuery ML to run several regression models, and analyze their performance.

B.

Read the data from BigQuery using Dataproc, and run several models using SparkML.

C.

Use Vertex AI Workbench user-managed notebooks with scikit-learn code for a variety of ML algorithms and performance metrics.

D.

Train a custom TensorFlow model with Vertex AI, reading the data from BigQuery featuring a variety of ML algorithms.

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