Win IT Exam with Last Dumps 2025

You have deployed multiple versions of an image classification model on AI Platform. You want to monitor the performance of the model versions over time. How should you perform this comparison?

A. Compare the loss performance for each model on a held-out dataset. B. Compare the loss performance for each model on the validation data. C. Compare the receiver operating characteristic (ROC) curve for each model using the What-If Tool. D. Compare the mean average precision across the models using the Continuous Evaluation feature.

You trained a text classification model. You have the following SignatureDefs:

You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers)
What is the correct way to write the predict request?

A. data = json.dumps({"signature_name": "seving_default", "instances" [['ab', 'bc', 'cd']]}) B. data = json.dumps({"signature_name": "serving_default", "instances" [['a', 'b', 'c', 'd', 'e', 'f']]}) C. data = json.dumps({"signature_name": "serving_default", "instances" [['a', 'b', 'c'], ['d', 'e', 'f']]}) D. data = json.dumps({"signature_name": "serving_default", "instances" [['a', 'b'], ['c', 'd'], ['e', 'f']]})

Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (PII) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

A. 1= Dataflow, 2= BigQuery B. 1 = Pub/Sub, 2= Datastore C. 1 = Dataflow, 2 = Cloud SQL D. 1 = Cloud Function, 2= Cloud SQL

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

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.

You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process. Which service should you use?

A. Dataflow B. Dataprep C. Apache Flink D. Cloud Data Fusion

You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

A. Redaction, reproducibility, and explainability B. Traceability, reproducibility, and explainability C. Federated learning, reproducibility, and explainability D. Differential privacy, federated learning, and explainability

You are training a Resnet model on AI Platform using TPUs to visually categorize types of defects in automobile engines. You capture the training profile using the
Cloud TPU profiler plugin and observe that it is highly input-bound. You want to reduce the bottleneck and speed up your model training process. Which modifications should you make to the tf.data dataset? (Choose two.)

A. Use the interleave option for reading data. B. Reduce the value of the repeat parameter. C. Increase the buffer size for the shuttle option. D. Set the prefetch option equal to the training batch size. E. Decrease the batch size argument in your transformation.

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 AI 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 AI 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 AI 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 AI 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 AI Platform using the transformed data. Write the predictions to an outbound Pub/Sub queue.

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.