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Professional-Machine-Learning-Engineer Exam Dumps - Google Machine Learning Engineer Questions and Answers

Question # 14

You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition model type color, and engine-'battery efficiency. The data is updated every night Car dealerships will use the model to determine appropriate car prices. You created a Vertex Al pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost What should you do?

Options:

A.

Compare the training and evaluation losses of the current run If the losses are similar, deploy the model to a Vertex AI endpoint Configure a cron job to redeploy the pipeline every night.

B.

Compare the training and evaluation losses of the current run If the losses are similar deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring When the model monitoring threshold is tnggered redeploy the pipeline.

C.

Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint Configure a cron job to redeploy the pipeline every night.

D.

Compare the results to the evaluation results from a previous run If the performance improved deploy the model to a Vertex Al endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered, redeploy the pipeline.

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Question # 15

You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

Options:

A.

Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.

B.

Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

C.

Rewrite your input function to resize and reshape the input images.

D.

Rewrite your input function using parallel reads, parallel processing, and prefetch.

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Question # 16

You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex Al endpoint. and validated that results were received in a reasonable amount of time After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests What should you do?

Options:

A.

Use a machine type with more memory

B.

Decrease the number of workers per machine

C.

Increase the CPU utilization target in the autoscaling configurations

D.

Decrease the CPU utilization target in the autoscaling configurations

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Question # 17

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?

Options:

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 # 18

You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

Options:

A.

Create a hot-encoding of words, and feed the encodings into your model.

B.

Identify word embeddings from a pre-trained model, and use the embeddings in your model.

C.

Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.

D.

Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.

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Question # 19

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?

Options:

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 # 20

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-learn, and custom libraries. What should you do?

Options:

A.

Use the Vertex AI Training to submit training jobs using any framework.

B.

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

C.

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

D.

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

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Question # 21

You are developing a training pipeline for a new XGBoost classification model based on tabular data The data is stored in a BigQuery table You need to complete the following steps

1. Randomly split the data into training and evaluation datasets in a 65/35 ratio

2. Conduct feature engineering

3 Obtain metrics for the evaluation dataset.

4 Compare models trained in different pipeline executions

How should you execute these steps'?

Options:

A.

1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering

2. Enable auto logging of metrics in the training component.

3 Compare pipeline runs in Vertex Al Experiments

B.

1 Using Vertex Al Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering

2 Enable autologging of metrics in the training component

3 Compare models using the artifacts lineage in Vertex ML Metadata

C.

1 In BigQuery ML. use the create model statement with bocstzd_tree_classifier as the model

type and use BigQuery to handle the data splits.

2 Use a SQL view to apply feature engineering and train the model using the data in that view

3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_infc statement.

D.

1 In BigQuery ML use the create model statement with boosted_tree_classifier as the model

type, and use BigQuery to handle the data splits.

2 Use ml transform to specify the feature engineering transformations, and train the model using the

data in the table

' 3. Compare the evaluation metrics of the models by using a SQL query with the ml. training_info statement.

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Question # 22

You are training an ML model on a large dataset. You are using a TPU to accelerate the training process You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

Options:

A.

Increase the learning rate

B.

Increase the number of epochs

C.

Decrease the learning rate

D.

Increase the batch size

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Question # 23

You have trained an XGBoost model that you plan to deploy on Vertex Al for online prediction. You are now uploading your model to Vertex Al Model Registry, and you need to configure the explanation method that will serve online prediction requests to be returned with minimal latency. You also want to be alerted when feature attributions of the model meaningfully change over time. What should you do?

Options:

A.

1 Specify sampled Shapley as the explanation method with a path count of 5.

2 Deploy the model to Vertex Al Endpoints.

3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

B.

1 Specify Integrated Gradients as the explanation method with a path count of 5.

2 Deploy the model to Vertex Al Endpoints.

3. Create a Model Monitoring job that uses prediction drift as the monitoring objective.

C.

1. Specify sampled Shapley as the explanation method with a path count of 50.

2. Deploy the model to Vertex Al Endpoints.

3. Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

D.

1 Specify Integrated Gradients as the explanation method with a path count of 50.

2. Deploy the model to Vertex Al Endpoints.

3 Create a Model Monitoring job that uses training-serving skew as the monitoring objective.

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Exam Name: Google Professional Machine Learning Engineer
Last Update: Apr 1, 2025
Questions: 285
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