def METHOD_NAME(sagemaker_session, cpu_instance_type, training_set):
    job_name = unique_name_from_base("pca-serverless")
    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
        pca = sagemaker.amazon.pca.PCA(
            role="SageMakerRole",
            instance_count=1,
            instance_type=cpu_instance_type,
            num_components=48,
            sagemaker_session=sagemaker_session,
            enable_network_isolation=True,
        )
        pca.algorithm_mode = "randomized"
        pca.subtract_mean = True
        pca.extra_components = 5
        pca.fit(pca.record_set(training_set[0][:100]), job_name=job_name)
    with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
        pca_model = sagemaker.amazon.pca.PCAModel(
            model_data=pca.model_data,
            role="SageMakerRole",
            sagemaker_session=sagemaker_session,
        )
        predictor = pca_model.deploy(
            serverless_inference_config=ServerlessInferenceConfig(), endpoint_name=job_name
        )
        result = predictor.predict(training_set[0][:5])
        assert len(result) == 5
        for record in result:
            assert record.label["projection"] is not None