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Google Professional Machine Learning Engineer Sample Questions (Q110-Q115):

NEW QUESTION # 110
You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure. What should you do?

• A. Significantly increase the maxenqueuedbatches TensorFlow Serving parameter
• B. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes
• C. Switch to the tensorflow-model-server-universal version of TensorFlow Serving
• D. Significantly increase the maxbatchsize TensorFlow Serving parameter

Answer: B
NEW QUESTION # 111
While performing exploratory data analysis on a dataset, you find that an important categorical feature has 5% null values. You want to minimize the bias that could result from the missing values. How should you handle the missing values?

• A. Replace the missing values with a placeholder category indicating a missing value.
• B. Replace the missing values with the feature's mean.
• C. Move the rows with missing values to your validation dataset.
• D. Remove the rows with missing values, and upsample your dataset by 5%.

Answer: B
NEW QUESTION # 112
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

• A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
• B. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
• C. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
• D. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job

Answer: D
NEW QUESTION # 113
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 dat a. How should you address the input differences in production?

• A. Retrain the model, and select an L2 regularization parameter with a hyperparameter tuning service
• B. Perform feature selection on the model, and retrain the model with fewer features
• C. Create alerts to monitor for skew, and retrain the model.
• D. Perform feature selection on the model, and retrain the model on a monthly basis with fewer features

Answer: C Explanation:
Data drift doesn't necessarily require feature reselection (e.g. by L2 regularization). https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#challenges Data values skews: These skews are significant changes in the statistical properties of data, which means that data patterns are changing, and you need to trigger a retraining of the model to capture these changes. https://developers.google.com/machine-learning/guides/rules-of-ml/#rule37measuretrainingservingskew
NEW QUESTION # 114
Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

• A. Binary cross-entropy
• B. Categorical cross-entropy
• C. Categorical hinge
• D. Sparse categorical cross-entropy

Answer: A
NEW QUESTION # 115
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