最新的Google Professional-Machine-Learning-Engineer免費考試真題

問題1

Your company's business stakeholders want to understand the factors driving customer churn to inform their business strategy. You need to build a customer churn prediction model that prioritizes simple interpretability of your model's results. You need to choose the ML framework and modeling technique that will explain which features led to the prediction. What should you do?

A. Build a logistic regression model in scikit-learn, and interpret the model's output coefficients to understand feature impact.

B. Build a linear regression model in scikit-learn, and interpret the model's standardized coefficients to understand feature impact.

C. Build a TensorFlow deep neural network (DNN) model, and use SHAP values for feature importance analysis.

D. Build a PyTorch long short-term memory (LSTM) network, and use attention mechanisms for interpretability.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題2

You are creating a retraining policy for a customer churn prediction model deployed in Vertex AI.
New training data is added weekly. You want to implement a model retraining process that minimizes cost and effort. What should you do?

A. Retrain the model when the model accuracy drops by 10% on the new training dataset.

B. Retrain the model every week when new training data is available.

C. Retrain the model when a significant shift in the distribution of customer attributes is detected in the production data compared to the training data.

D. Retrain the model when the model's latency increases by 10% due to increased traffic.

正確答案: C

說明：（僅 Fast2test 成員可見）

問題3

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am.
The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

A. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.
2. Dispatch an appropriately sized shuttle and indicate the required stops on the map.

B. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the prediction.

C. 1. Build a reinforcement learning model with tree-based classification models that predict the presence of passengers at shuttle stops as agents and a reward function around a distance- based metric.
2. Dispatch an appropriately sized shuttle and provide the map with the required stops based on the simulated outcome.

D. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.
2. Dispatch an available shuttle and provide the map with the required stops based on the prediction.

正確答案: B

問題4

You work for a bank. You need to train a model by using unstructured data stored in Cloud Storage that predicts whether credit card transactions are fraudulent. The data needs to be converted to a structured format to facilitate analysis in BigQuery. Company policy requires that data containing personally identifiable information (PII) remain in Cloud Storage. You need to implement a scalable solution that preserves the data's value for analysis. What should you do?

A. Remove the sensitive data from the files manually before loading them into BigQuery.

B. Use the DLP API to de-identify the sensitive data before loading it into BigQuery.

C. Use BigQuery's authorized views and column-level access controls to restrict access to PII within the dataset.

D. Store the unstructured data in a separate PII-compliant BigQuery database.

正確答案: B

說明：（僅 Fast2test 成員可見）

問題5

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. Increase the minReplicaCount in your DeployedModel configuration

B. Increase the number of workers in your model server

C. Attach a GPU to the prediction nodes

D. Schedule scaling of the nodes to match expected demand

正確答案: B

問題6

Your production TensorFlow pipeline computes vocabulary mappings and z-score normalization statistics over the full training dataset. Engineers reimplemented the same transformations in the serving application, and subtle differences have caused prediction errors twice this year. You need to guarantee that serving transformations exactly match training transformations. What should you do?

A. Write unit tests in the serving application that compare its output against sample training records.

B. Compute normalization statistics on each incoming serving batch so the values are always current.

C. Document the transformation logic in a shared design document that both teams review before each release.

D. Use TensorFlow Transform to compute the statistics in the pipeline and emit a transform graph that is attached to the exported SavedModel for serving.

正確答案: D

說明：（僅 Fast2test 成員可見）

問題7

You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

A. 1. Use Vertex AI Experiments to train your model.
2. Register your model in Vertex AI Model Registry.
3. Generate batch predictions in Vertex AI.

B. 1. Upload your dataset to BigQuery.
2. Use a Vertex AI custom training job to train your model.
3. Generate predictions by using Vertex Al SDK custom prediction routines.

C. 1. Use a Vertex AI Pipelines custom training job component to tram your model.
2. Generate predictions by using a Vertex AI Pipelines model batch predict component.

D. 1. Create a Vertex AI managed dataset.
2. Use a Vertex AI training pipeline to train your model.
3. Generate batch predictions in Vertex AI.

正確答案: A

問題8

You are building a model that predicts whether a customer will cancel a subscription within the next 30 days. Cross-validation accuracy is 0.97, but production performance drops to 0.61.
Reviewing the feature list, you find a column named days_since_last_login that is computed at the time the training table is generated, and a column named cancellation_survey_completed.
You need to correct the root cause. What should you do?

A. Increase the size of the training dataset and retrain.

B. Add regularization and reduce model complexity to close the gap between validation and production performance.

C. Replace k-fold cross-validation with a single random 80/20 split.

D. Remove features that are unavailable or that reflect post-outcome information at prediction time, and rebuild the training set using point-in-time correct feature values.

正確答案: D

說明：（僅 Fast2test 成員可見）

問題9

You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100,000 categorical features. You notice that as the data increases, the model training time increases. You plan to move the models to Google Cloud. You want to use the most scalable approach that also minimizes training time. What should you do?

A. Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs

B. Deploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbeading API

C. Deploy the training jobs by using Compute Engine instances with A100 GPUs, and use the tf.nn.embedding_lookup API

D. Deploy a matrix factorization model training job by using BigQuery ML

正確答案: B

問題10

You work at a mobile gaming startup that creates online multiplayer games. Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated. Your model has performed well during testing, and you now need to deploy the model to production. You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A. Save the model files in a VM. Load the model files each time there is a prediction request, and run an inference job on the VM

B. Save the model files in a Cloud Storage bucket. Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

C. Import the model into Vertex AI Model Registry. Create a Vertex AI endpoint that hosts the model, and make online inference requests.

D. Import the model into Vertex AI Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

正確答案: C

問題11

You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

A. Use Vertex AI Workbench user-managed notebooks to generate the report.

B. Use the output from TensorFlow Data Validation on Dataflow to generate the report.

C. Use Dataprep to create the report.

D. Use the Google Data Studio to create the report.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題12

You work for an international bank. Your team is developing a generative AI application that summarizes complex financial reports. You need to implement an automated, scalable solution to evaluate and quantify the quality of generated summaries against the previous version of the solution. What should you do?

A. Run an evaluation job in Agent Platform, and calculate a BLEU score to compare the new and previous versions of the solution.

B. Run an automatic side-by-side (AutoSxS) evaluation job in Agent Platform to compare outputs from the new and previous versions of the solution.

C. Deploy both versions of the solution, and use A/B testing to route a portion of production traffic to the new version and route the remaining traffic to the previous version. Compare user click- through rates.

D. Use Model Monitoring to track the prediction drift of the token output length.

正確答案: B

說明：（僅 Fast2test 成員可見）

問題13

You have trained a text classification model in TensorFlow using AI Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead.
What should you do?

A. Deploy and version the model on AI Platform.

B. Export the model to BigQuery ML.

C. Submit a batch prediction job on AI Platform that points to the model location in Cloud Storage.

D. Use Dataflow with the SavedModel to read the data from BigQuery.

正確答案: B

說明：（僅 Fast2test 成員可見）

問題14

You are developing an ML model on Vertex AI that needs to meet specific interpretability requirements for regulatory compliance. You want to use a combination of model architectures and modeling techniques to maximize accuracy and interpretability. How should you create the model?

A. Use a boosted decision tree-based model architecture, and use SHAP values for interpretability.

B. Use a recurrent neural network (RNN)-based deep learning model architecture, and use integrated gradients for interpretability.

C. Use a long short-term memory (LSTM)-based model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.

D. Use a convolutional neural network (CNN)-based deep learning model architecture, and use local interpretable model-agnostic explanations (LIME) for interpretability.

正確答案: A

說明：（僅 Fast2test 成員可見）

問題15

You are developing a model to help your company create more targeted online advertising campaigns. You need to create a dataset that you will use to train the model. You want to avoid creating or reinforcing unfair bias in the model. What should you do? (Choose two.)

A. Collect a stratified sample of production traffic to build the training dataset

B. Collect a random sample of production traffic to build the training dataset

C. Conduct fairness tests across sensitive categories and demographics on the trained model

D. Include a comprehensive set of demographic features

E. Include only the demographic groups that most frequently interact with advertisements

正確答案: A

最新的Google Professional Machine Learning Engineer - Professional-Machine-Learning-Engineer免費考試真題

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