最新的Google Cloud Certified認證 Professional-Machine-Learning-Engineer考試題庫

問題1

You are investigating the root cause of a misclassification error made by one of your models. You used Vertex AI Pipelines to train and deploy the model. The pipeline reads data from BigQuery.
creates a copy of the data in Cloud Storage in TFRecord format, trains the model in Vertex AI Training on that copy, and deploys the model to a Vertex AI endpoint. You have identified the specific version of that model that misclassified, and you need to recover the data this model was trained on. How should you find that copy of the data?

A. Find the job ID in Vertex AI Training corresponding to the training for the model. Search in the logs of that job for the data used for the training.

B. Use the logging features in the Vertex AI endpoint to determine the timestamp of the model's deployment. Find the pipeline run at that timestamp. Identify the step that creates the data copy, and search in the logs for its location.

C. Use the lineage feature of Vertex AI Metadata to find the model artifact. Determine the version of the model and identify the step that creates the data copy and search in the metadata for its location.

D. Use Vertex AI Feature Store. Modify the pipeline to use the feature store, and ensure that all training data is stored in it. Search the feature store for the data used for the training.

正確答案: C

問題2

You work for a retail company. You have a managed tabular dataset in Vertex AI that contains sales data from three different stores. The dataset includes several features, such as store name and sale timestamp. You want to use the data to train a model that makes sales predictions for a new store that will open soon. You need to split the data between the training, validation, and test sets. What approach should you use to split the data?

A. Use Vertex AI chronological split, and specify the sales timestamp feature as the time variable

B. Use Vertex AI manual split, using the store name feature to assign one store for each set

C. Use Vertex AI default data split

D. Use Vertex AI random split, assigning 70% of the rows to the training set, 10% to the validation set, and 20% to the test set

正確答案: A

說明：（僅 VCESoft 成員可見）

問題3

You developed an ML model with AI 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. Recompile TensorFlow Serving using the source to support CPU-specific optimizations.
Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes.

B. Significantly increase the max_batch_size TensorFlow Serving parameter.

C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter.

D. Switch to the tensorflow-model-server-universal version of TensorFlow Serving.

正確答案: A

說明：（僅 VCESoft 成員可見）

問題4

While monitoring your model training's GPU utilization, you discover that you have a native synchronous implementation. The training data is split into multiple files. You want to reduce the execution time of your input pipeline. What should you do?

A. Add caching to the pipeline

B. Add parallel interleave to the pipeline

C. Increase the CPU load

D. Increase the network bandwidth

正確答案: B

說明：（僅 VCESoft 成員可見）

問題5

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 20 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. Submit the data for training without performing any manual transformations, and indicate an appropriate column as the Time column.
AIlow AutoML to split your data based on the time signal provided, and reserve the more recent data for the validation and testing sets.

B. 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 sets from 30 days after your validation set.

C. Manually combine all columns that contain a time signal into an array.
AIlow AutoML to interpret this array appropriately.
Choose an automatic data split across the training, validation, and testing sets.

D. Submit the data for training without performing any manual transformations.
AIlow AutoML to handle the appropriate transformations.
Choose an automatic data split across the training, validation, and testing sets.

正確答案: B

說明：（僅 VCESoft 成員可見）

問題6

You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

A. Address data leakage by removing features highly correlated with the target value.

B. Address the model overfitting by using a less complex algorithm.

C. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

D. Address data leakage by applying nested cross-validation during model training.

正確答案: D

說明：（僅 VCESoft 成員可見）

問題7

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

B. Deploy and version the model on AI Platform.

C. Export the model to BigQuery ML.

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

正確答案: C

說明：（僅 VCESoft 成員可見）

問題8

You are developing a natural language processing model that analyzes customer feedback to identify positive, negative, and neutral experiences. During the testing phase, you notice that the model demonstrates a significant bias against certain demographic groups, leading to skewed analysis results. You want to address this issue following Google's responsible AI practices. What should you do?

A. Implement a more complex model architecture that can capture nuanced patterns in language to reduce bias.

B. Use Vertex AI's model evaluation to assess bias in the model's predictions, and use post- processing to adjust outputs for identified demographic discrepancies.

C. Use Vertex Explainable AI to generate explanations and systematically adjust the predictions to address identified biases.

D. Audit the training dataset to identify underrepresented groups and augment the dataset with additional samples before retraining the model.

正確答案: D

說明：（僅 VCESoft 成員可見）

問題9

You work for a bank. You have created a custom model to predict whether a loan application should be flagged for human review. The input features are stored in a BigQuery table. The model is performing well, and you plan to deploy it to production. Due to compliance requirements the model must provide explanations for each prediction. You want to add this functionality to your model code with minimal effort and provide explanations that are as accurate as possible.
What should you do?

A. Upload the custom model to Vertex AI Model Registry and configure feature-based attribution by using sampled Shapley with input baselines.

B. Create a BigQuery ML deep neural network model and use the ML.EXPLAIN_PREDICT method with the num_integral_steps parameter.

C. Create an AutoML tabular model by using the BigQuery data with integrated Vertex Explainable AI.

D. Update the custom serving container to include sampled Shapley-based explanations in the prediction outputs.

正確答案: D

問題10

Your company manages an ecommerce platform and has a large dataset of customer reviews.
Each review has a positive, negative, or neutral label. You need to quickly prototype a sentiment analysis model that accurately predicts the sentiment labels of new customer reviews while minimizing time and cost. What should you do?

A. Use Agent Platform AutoML to train a multi-class classification model that predicts sentiment labels based on the training data.

B. Use the Agent Platform Text Embeddings API to vectorize the text, and train a regression model by using Agent Platform AutoML to predict sentiment scores.

C. Use the Natural Language API for real-time sentiment analysis.

D. Train a sentiment analysis model by using a BERT-based model, and fine-tune the model by using domain-specific customer reviews.

正確答案: A

說明：（僅 VCESoft 成員可見）

問題11

You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website. You plan to deploy the model to a new Vertex AI endpoint that will integrate with your website application. You expect higher traffic to the website during nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?

A. Configure the model deployment settings to use an n1-standard-32machine type.

B. Configure the model deployment settings to use an n1-standard-4machine type. Set the minReplicaCountvalue to 1 and the maxReplicaCountvalue to 8.

C. Configure the model deployment settings to use an n1-standard-8machine type and a GPU accelerator.

D. Configure the model deployment settings to use an n1-standard-4machine type and a GPU accelerator. Set the minReplicaCountvalue to 1 and the maxReplicaCountvalue to 4.

正確答案: B

說明：（僅 VCESoft 成員可見）

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