Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Practice Questions and Exam Preparation

Google PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Questions & Answers

• Question 191:

  You work for an international manufacturing organization that ships scientific products all over the world. Instruction manuals for these products need to be translated to 15 different languages. Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?

  A. Create a workflow using Cloud Function triggers. Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket. Configure another Cloud Function that translates the documents using the Cloud Translation API, and saves the translations to an output Cloud Storage bucket. Use human reviewers to evaluate the incorrect translations.
  B. Create a Vertex AI pipeline that processes the documents launches, an AutoML Translation training job, evaluates the translations and deploys the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between training and live data, re-trigger the pipeline with the latest data.
  C. Use AutoML Translation to train a model. Configure a Translation Hub project, and use the trained model to translate the documents. Use human reviewers to evaluate the incorrect translations.
  D. Use Vertex AI custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data. Deploy the model to a Vertex AI endpoint with autoscaling and model monitoring. When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
  C. Use AutoML Translation to train a model. Configure a Translation Hub project, and use the trained model to translate the documents. Use human reviewers to evaluate the incorrect translations.

• Question 192:

  You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?

  A. Standardize the data by transforming it with a logarithmic function.
  B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.
  C. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.
  D. Normalize the data by scaling it to have values between 0 and 1.
  A. Standardize the data by transforming it with a logarithmic function.

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  Explanation/Reference:

  https://developers.google.com/machine-learning/data-prep/transform/normalization

• Question 193:

  During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

  A. Decrease the size of the training batch.
  B. Decrease the learning rate hyperparameter.
  C. Increase the learning rate hyperparameter.
  D. Increase the size of the training batch.
  B. Decrease the learning rate hyperparameter.

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  Explanation/Reference:

  https://ai.stackexchange.com/questions/14079/what-could-an-oscillating-training-loss-curve-represent#:~:text=Try%20lowering%20the%20learning%20rate,step%20and%20overshoot%20it%20again.

• Question 194:

  You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12 hours. How should you design the architecture?

  A. 1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction 2. Your application queries a Vertex AI endpoint where you deployed your model. 3. Responses are received by the caller application as soon as the model produces the prediction.
  B. 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline. 2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic. 3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.
  C. 1. Export your data to Cloud Storage using Dataflow. 2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data. 3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
  D. 1. Export the data to Cloud Storage using the BigQuery command-line tool 2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data. 3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
  B. 1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline. 2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic. 3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.

• Question 195:

  You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories. You have a labeled dataset in Cloud Storage. You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency. What should you do?

  A. Train the model by using AutoML, and register the model in Vertex AI Model Registry. Configure your mobile application to send batch requests during prediction.
  B. Train the model by using AutoML Edge, and export it as a Core ML model. Configure your mobile application to use the .mlmodel file directly.
  C. Train the model by using AutoML Edge, and export the model as a TFLite model. Configure your mobile application to use the .tflite file directly.
  D. Train the model by using AutoML, and expose the model as a Vertex AI endpoint. Configure your mobile application to invoke the endpoint during prediction.
  B. Train the model by using AutoML Edge, and export it as a Core ML model. Configure your mobile application to use the .mlmodel file directly.

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  Explanation/Reference:

• Question 196:

  You are working on a binary classification ML algorithm that detects whether an image of a classified scanned document contains a company's logo. In the dataset, 96% of examples don't have the logo, so the dataset is very skewed. Which metric would give you the most confidence in your model?

  A. Precision
  B. Recall
  C. RMSE
  D. F1 score
  D. F1 score

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  Explanation/Reference:

  https://stephenallwright.com/imbalanced-data-metric/

• Question 197:

  You have been tasked with deploying prototype code to production. The feature engineering code is in PySpark and runs on Dataproc Serverless. The model training is executed by using a Vertex AI custom training job. The two steps are not connected, and the model training must currently be run manually after the feature engineering step finishes. You need to create a scalable and maintainable production process that runs end-to-end and tracks the connections between steps. What should you do?

  A. Create a Vertex AI Workbench notebook. Use the notebook to submit the Dataproc Serverless feature engineering job. Use the same notebook to submit the custom model training job. Run the notebook cells sequentially to tie the steps together end-to-end.
  B. Create a Vertex AI Workbench notebook. Initiate an Apache Spark context in the notebook and run the PySpark feature engineering code. Use the same notebook to run the custom model training job in TensorFlow. Run the notebook cells sequentially to tie the steps together end-to-end.
  C. Use the Kubeflow pipelines SDK to write code that specifies two components: 1. The first is a Dataproc Serverless component that launches the feature engineering job 2. The second is a custom component wrapped in the create_custom_training_job_from_component utility that launches the custom model training job Create a Vertex AI Pipelines job to link and run both components
  D. Use the Kubeflow pipelines SDK to write code that specifies two components 1. The first component initiates an Apache Spark context that runs the PySpark feature engineering code 2. The second component runs the TensorFlow custom model training code Create a Vertex AI Pipelines job to link and run both components.
  C. Use the Kubeflow pipelines SDK to write code that specifies two components: 1. The first is a Dataproc Serverless component that launches the feature engineering job 2. The second is a custom component wrapped in the create_custom_training_job_from_component utility that launches the custom model training job Create a Vertex AI Pipelines job to link and run both components

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  Explanation/Reference:

• Question 198:

  You have recently trained a scikit-learn model that you plan to deploy on Vertex AI. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code. What should you do?

  A. 1. Upload your model to the Vertex AI Model Registry by using a prebuilt scikit-ieam prediction container. 2. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
  B. 1. Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model. 2. Upload your scikit learn model container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job
  C. 1. Create a custom container for your scikit learn model. 2. Define a custom serving function for your model. 3. Upload your model and custom container to Vertex AI Model Registry. 4. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job.
  D. 1. Create a custom container for your scikit learn model. 2. Upload your model and custom container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job that uses the instanceConfig.instanceType setting to transform your input data.
  B. 1. Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model. 2. Upload your scikit learn model container to Vertex AI Model Registry. 3. Deploy your model to Vertex AI Endpoints, and create a Vertex AI batch prediction job

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  Explanation/Reference:

• Question 199:

  You are creating an ML pipeline for data processing, model training, and model deployment that uses different Google Cloud services. You have developed code for each individual task, and you expect a high frequency of new files. You now need to create an orchestration layer on top of these tasks. You only want this orchestration pipeline to run if new files are present in your dataset in a Cloud Storage bucket. You also want to minimize the compute node costs. What should you do?

  A. Create a pipeline in Vertex AI Pipelines. Configure the first step to compare the contents of the bucket to the last time the pipeline was run. Use the scheduler API to run the pipeline periodically.
  B. Create a Cloud Function that uses a Cloud Storage trigger and deploys a Cloud Composer directed acyclic graph (DAG).
  C. Create a pipeline in Vertex AI Pipelines. Create a Cloud Function that uses a Cloud Storage trigger and deploys the pipeline.
  D. Deploy a Cloud Composer directed acyclic graph (DAG) with a GCSObjectUpdateSensor class that detects when a new file is added to the Cloud Storage bucket.
  C. Create a pipeline in Vertex AI Pipelines. Create a Cloud Function that uses a Cloud Storage trigger and deploys the pipeline.

• Question 200:

  You are training models in Vertex AI by using data that spans across multiple Google Cloud projects. You need to find, track, and compare the performance of the different versions of your models. Which Google Cloud services should you include in your ML workflow?

  A. Dataplex, Vertex AI Feature Store, and Vertex AI TensorBoard
  B. Vertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments
  C. Dataplex, Vertex AI Experiments, and Vertex AI ML Metadata
  D. Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Metadata
  D. Vertex AI Pipelines, Vertex AI Experiments, and Vertex AI Metadata

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  Explanation/Reference: