PROFESSIONAL-MACHINE-LEARNING-ENGINEER Online Practice Questions and Answers

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?

A. Use the AI Platform custom containers feature to receive training jobs using any framework.

B. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TF Job.

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.

You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to AI Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the AI Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?

A. Increase the recall.

B. Decrease the recall.

C. Increase the number of false positives.

D. Decrease the number of false negatives.

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 data. How should you address the input differences in production?

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

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

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

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

You work for an online travel agency that also sells advertising placements on its website to other companies. You have been asked to predict the most relevant web banner that a user should see next. Security is important to your company. The model latency requirements are 300ms@p99, the inventory is thousands of web banners, and your exploratory analysis has shown that navigation context is a good predictor. You want to Implement the simplest solution. How should you configure the prediction pipeline?

A. Embed the client on the website, and then deploy the model on AI Platform Prediction.

B. Embed the client on the website, deploy the gateway on App Engine, and then deploy the model on AI Platform Prediction.

C. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Cloud Bigtable for writing and for reading the user's navigation context, and then deploy the model on AI Platform Prediction.

D. Embed the client on the website, deploy the gateway on App Engine, deploy the database on Memorystore for writing and for reading the user's navigation context, and then deploy the model on Google Kubernetes Engine.

You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

A. Use then TFX ModelValidator tools to specify performance metrics for production readiness.

B. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.

C. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data.

D. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

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.

You recently deployed a scikit-learn model to a Vertex AI endpoint. You are now testing the model on live production traffic. While monitoring the endpoint, you discover twice as many requests per hour than expected throughout the day. You want the endpoint to efficiently scale when the demand increases in the future to prevent users from experiencing high latency. What should you do?

A. Deploy two models to the same endpoint, and distribute requests among them evenly

B. Configure an appropriate minReplicaCount value based on expected baseline traffic

C. Set the target utilization percentage in the autoscailngMetricSpecs configuration to a higher value

D. Change the model's machine type to one that utilizes GPUs

You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII). You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. What should you do?

A. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PII before performing data exploration and preprocessing.

B. Use customer-managed encryption keys (CMEK) to encrypt the PII data at rest, and decrypt the PII data during data exploration and preprocessing.

C. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.

D. Use Google-managed encryption keys to encrypt the PII data at rest, and decrypt the PII data during data exploration and preprocessing.

You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions. Recently you developed a new version of the model that uses a different architecture (custom model). Initial analysis revealed that both models are performing as expected. You want to deploy the new version of the model to production and monitor the performance over the next two months. You need to minimize the impact to the existing and future model users. How should you deploy the model?

A. Import the new model to the same Vertex AI Model Registry as a different version of the existing model. Deploy the new model to the same Vertex AI endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.

B. Import the new model to the same Vertex AI Model Registry as the existing model. Deploy the models to one Vertex AI endpoint. Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.

C. Import the new model to the same Vertex AI Model Registry as the existing model. Deploy each model to a separate Vertex AI endpoint.

D. Deploy the new model to a separate Vertex AI endpoint. Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.

You have deployed a scikit-team model to a Vertex AI endpoint using a custom model server. You enabled autoscaling: however, the deployed model fails to scale beyond one replica, which led to dropped requests. You notice that CPU utilization remains low even during periods of high load. What should you do?

A. Attach a GPU to the prediction nodes

B. Increase the number of workers in your model server

C. Schedule scaling of the nodes to match expected demand

D. Increase the minReplicaCount in your DeployedModel configuration