What Precise Professional-Machine-Learning-Engineer Vce Is

NEW QUESTION 1
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers

• A. Decrease the number of parallel trials
• B. Decrease the range of floating-point values
• C. Set the early stopping parameter to TRUE
• D. Change the search algorithm from Bayesian search to random search.
• E. Decrease the maximum number of trials during subsequent training phases.

Answer: DE

NEW QUESTION 2
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

• A. Build a classification model
• B. Build a knowledge-based filtering model
• C. Build a collaborative-based filtering model
• D. Build a regression model using the features as predictors

Answer: C

NEW QUESTION 3
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

• A. • Validate the accuracy of the model that you trained on preprocessed data• Create a new model that uses the raw data and is available in real time• Deploy the new model onto Al Platform for online prediction
• B. • Send incoming prediction requests to a Pub/Sub topic• Transform the incoming data using a Dataflow job• Submit a prediction request to Al Platform using the transformed data• Write the predictions to an outbound Pub/Sub queue
• C. • Stream incoming prediction request data into Cloud Spanner• Create a view to abstract your preprocessing logic.• Query the view every second for new records• Submit a prediction request to Al Platform using the transformed data• Write the predictions to an outbound Pub/Sub queue.
• D. • Send incoming prediction requests to a Pub/Sub topic• Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.• Implement your preprocessing logic in the Cloud Function• Submit a prediction request to Al Platform using the transformed data• Write the predictions to an outbound Pub/Sub queue

Answer: D

NEW QUESTION 4
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?

• A. Use the "Other Products You May Like" recommendation type to increase the click-through rate
• B. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
• C. Import your user events and then your product catalog to make sure you have the highest quality event stream
• D. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.

Answer: C

NEW QUESTION 5
You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases. Which learning strategy should you recommend to train and deploy this ML model?

• A. Differential privacy
• B. Federated learning
• C. MD5 to encrypt data
• D. Data Loss Prevention API

Answer: B

NEW QUESTION 6
You were asked to investigate failures of a production line component based on sensor readings. After receiving the dataset, you discover that less than 1% of the readings are positive examples representing failure incidents. You have tried to train several classification models, but none of them converge. How should you resolve the class imbalance problem?

• A. Use the class distribution to generate 10% positive examples
• B. Use a convolutional neural network with max pooling and softmax activation
• C. Downsample the data with upweighting to create a sample with 10% positive examples
• D. Remove negative examples until the numbers of positive and negative examples are equal

Answer: D

NEW QUESTION 7
You work for a large hotel chain and have been asked to assist the marketing team in gathering predictions for a targeted marketing strategy. You need to make predictions about user lifetime value (LTV) over the next 30 days so that marketing can be adjusted accordingly. The customer dataset is in BigQuery, and you are preparing the tabular data for training with AutoML Tables. This data has a time signal that is spread across multiple columns. How should you ensure that AutoML fits the best model to your data?

• A. Manually combine all columns that contain a time signal into an array Allow AutoML to interpret this array appropriatelyChoose an automatic data split across the training, validation, and testing sets
• B. Submit the data for training without performing any manual transformations Allow AutoML to handle the appropriatetransformations Choose an automatic data split across the training, validation, and testing sets
• C. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column Allow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets
• D. Submit the data for training without performing any manual transformations Use the columns that have a time signal to manually split your data Ensure that the data in your validation set is from 30 days after the data in your training set and that the data in your testing set is from 30 days after your validation set

Answer: D

NEW QUESTION 8
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

Answer: C

NEW QUESTION 9
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

• A. AutoML Vision model
• B. AutoML Vision Edge mobile-versatile-1 model
• C. AutoML Vision Edge mobile-low-latency-1 model
• D. AutoML Vision Edge mobile-high-accuracy-1 model

Answer: A

NEW QUESTION 10
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 the 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.

Answer: A

NEW QUESTION 11
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard. What should you do?

• A. Create multiple models using AutoML Tables
• B. Automate multiple training runs using Cloud Composer
• C. Run multiple training jobs on Al Platform with similar job names
• D. Create an experiment in Kubeflow Pipelines to organize multiple runs

Answer: C

NEW QUESTION 12
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?

• A. Create a tf.data.Dataset.prefetch transformation
• B. Convert the images to tf .Tensor Objects, and then run Datase
• C. from_tensor_slices{).
• D. Convert the images to tf .Tensor Objects, and then run t
• E. dat
• F. Datase
• G. from_tensors ().
• H. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the t
• I. data API to read the images for training

Answer: D

NEW QUESTION 13
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. Increase the size of the training batch
• B. Decrease the size of the training batch
• C. Increase the learning rate hyperparameter
• D. Decrease the learning rate hyperparameter

Answer: C

NEW QUESTION 14
Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time. What should they use to track and report their experiments while minimizing manual effort?

• A. Use Kubeflow Pipelines to execute the experiments Export the metrics file, and query the results using the Kubeflow Pipelines API.
• B. Use Al Platform Training to execute the experiments Write the accuracy metrics to BigQuery, and query the results using the BigQueryAPI.
• C. Use Al Platform Training to execute the experiments Write the accuracy metrics to Cloud Monitoring, and query the results using the Monitoring API.
• D. Use Al Platform Notebooks to execute the experiment
• E. Collect the results in a shared Google Sheetsfile, and query the results using the Google Sheets API

Answer: A

NEW QUESTION 15
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
• Optimizer: SGD
• Image shape = 224x224
• Batch size = 64
• Epochs = 10
• Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

• A. Change the optimizer
• B. Reduce the batch size
• C. Change the learning rate
• D. Reduce the image shape

Answer: A

NEW QUESTION 16
......