Professional-Machine-Learning-Engineer 試験問題を無料オンラインアクセス

You work for an auto insurance company. You are preparing a proof-of-concept ML application that uses images of damaged vehicles to infer damaged parts. Your team has assembled a set of annotated images from damage claim documents in the company's database. The annotations associated with each image consist of a bounding box for each identified damaged part and the part name. You have been given a sufficient budget to train models on Google Cloud. You need to quickly create an initial model. What should you do?

• A.Train an object detection model in Vertex AI custom training by using the annotated image data.
• B.Create a pipeline in Vertex AI Pipelines and configure the AutoMLTrainingJobRunOp component to train a custom object detection model by using the annotated image data.
• C.Train an object detection model in AutoML by using the annotated image data.
• D.Download a pre-trained object detection model from TensorFlow Hub. Fine-tune the model in Vertex AI Workbench by using the annotated image data.

You are the lead ML engineer for a large ecommerce company. The Data Analytics team has prepared a dataset of 50 million transaction rows, which is stored in a partitioned BigQuery table.
The team consists primarily of SQL experts with limited experience in Python or TensorFlow.
You need to build a binary classification model to predict the likelihood of a customer making a purchase in the next seven days. You need to provide daily batch predictions. You need to minimize development effort, data movement, and the overall complexity of the solution. What should you do?

• A.Develop an XGBoost model on an Agent Platform Workbench notebook. Load the data into a pandas DataFrame, and pickle it to a Cloud Storage bucket. Create another notebook that loads the pickled model from Cloud Storage and saves predictions back to Cloud Storage. Schedule this notebook for nightly execution.
• B.Use the BigQuery ML TRANSFORM function to normalize and scale numerical features, one-hot encode categorical features, and train a MODEL_TYPE=LOGISTIC_REG mode. Use the ML.PREDICT function to generate predictions as part of your nightly data warehouse jobs.
• C.Create a model with BigQuery ML that includes the MODEL_TYPE=AUTOML_CLASSIFIER settings. Use the ML.PREDICT function to generate predictions as part of your nightly data warehouse jobs.
• D.Train a classification model with Agent Platform AutoML Tabular Workflows, deploy the trained model to an Agent Platform endpoint, and add a nightly Cloud Run job to trigger batch predictions.

Your organization manages an online message board. A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive. Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

• A.Add synthetic training data where those phrases are used in non-toxic ways.
• B.Remove the model and replace it with human moderation.
• C.Replace your model with a different text classifier.
• D.Raise the threshold for comments to be considered toxic or harmful.

You are training an LSTM-based model on AI Platform to summarize text using the following job submission script:
gcloud ai-platform jobs submit training $JOB_NAME \
--package-path $TRAINER_PACKAGE_PATH \
--module-name $MAIN_TRAINER_MODULE \
--job-dir $JOB_DIR \
--region $REGION \
--scale-tier basic \
-- \
--epochs 20 \
--batch_size=32 \
--learning_rate=0.001 \
You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

• A.Modify the 'scale-tier' parameter.
• B.Modify the 'learning rate' parameter.
• C.Modify the 'epochs' parameter.
• D.Modify the 'batch size' parameter.

You work at a large organization that recently decided to move their ML and data workloads to Google Cloud. The data engineering team has exported the structured data to a Cloud Storage bucket in Avro format. You need to propose a workflow that performs analytics, creates features, and hosts the features that your ML models use for online prediction. How should you configure the pipeline?

• A.Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.
• B.Ingest the Avro files into BigQuery to perform analytics. Use BigQuery SQL to create features and store them in a separate BigQuery table for online prediction.
• C.Ingest the Avro files into BigQuery to perform analytics. Use a Dataflow pipeline to create the features, and store them in Vertex AI Feature Store for online prediction.
• D.Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features, and store them in BigQuery for online prediction.