최신 Professional-Machine-Learning-Engineer 무료덤프 - Google Professional Machine Learning Engineer

문제1

You developed a Vertex Al pipeline that trains a classification model on data stored in a large BigQuery table.
The pipeline has four steps, where each step is created by a Python function that uses the KubeFlow v2 API The components have the following names:
You launch your Vertex Al pipeline as the following:
You perform many model iterations by adjusting the code and parameters of the training step.
You observe high costs associated with the development, particularly the data export and preprocessing steps.
You need to reduce model development costs.
What should you do?

A.

B.

C.

D.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제2

You are developing an ML pipeline using Vertex Al Pipelines. You want your pipeline to upload a new version of the XGBoost model to Vertex Al Model Registry and deploy it to Vertex Al End points for online inference. You want to use the simplest approach. What should you do?

A. Use the Vertex Al ModelEvaluationOp component to evaluate the model.

B. Chain the Vertex Al ModelUploadOp and ModelDeployop components together.

C. Use the Vertex Al SDK for Python within a custom component based on a python: 3.10 Image.

D. Use the Vertex Al REST API within a custom component based on a vertex-ai/prediction/xgboost-cpu image.

정답: B

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문제3

You are a data scientist at an industrial equipment manufacturing company. You are developing a regression model to estimate the power consumption in the company's manufacturing plants based on sensor data collected from all of the plants. The sensors collect tens of millions of records every day. You need to schedule daily training runs for your model that use all the data collected up to the current date. You want your model to scale smoothly and require minimal development work. What should you do?

A. Develop a regression model using BigQuery ML.

B. Develop a custom TensorFlow regression model, and optimize it using Vertex Al Training.

C. Develop a custom scikit-learn regression model, and optimize it using Vertex Al Training

D. Develop a custom PyTorch regression model, and optimize it using Vertex Al Training

정답: A

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문제4

Your task is classify if a company logo is present on an image. You found out that 96% of a data does not include a logo. You are dealing with data imbalance problem. Which metric do you use to evaluate to model?

A. RMSE

B. F1 Score

C. F Score with higher precision weighting than recall

D. F Score with higher recall weighted than precision

정답: B

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문제5

You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

A. Identify word embeddings from a pre-trained model, and use the embeddings in your model.

B. Create a hot-encoding of words, and feed the encodings into your model.

C. Sort the words by frequency of occurrence, and use the frequencies as the encodings in your model.

D. Assign a numerical value to each word from 1 to 100,000 and feed the values as inputs in your model.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제6

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

What is the correct way to write the predict request?

A. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})

B. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b\ 'c'1, [d\ 'e\ T]]})

C. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c\ 'd'], ['e\ T]]})

D. data = json.dumps({"signature_name": "serving_default'\ "instances": [fab', 'be1, 'cd']]})

정답: C

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문제7

You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

A. Use the BigQuery API Connector and Cloud Scheduler to trigger. Workflows every week that retrains the model.

B. Use BigQuerys scheduling service to run the model retraining query periodically.

C. Create a pipeline in Vertex Al Pipelines that executes the retraining query and use the Cloud Scheduler API to run the query weekly.

D. Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제8

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

A. Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

B. Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

C. Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

D. Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제9

You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?

A. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.

B. Use AI Platform to run distributed training jobs with checkpoints.

C. Use AI Platform to run distributed training jobs without checkpoints.

D. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제10

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

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

C. 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 set is from 30 days after your validation set

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

정답: B

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문제11

You work for a company that is developing an application to help users with meal planning You want to use machine learning to scan a corpus of recipes and extract each ingredient (e g carrot, rice pasta) and each kitchen cookware (e.g. bowl, pot spoon) mentioned Each recipe is saved in an unstructured text file What should you do?

A. Use the Entity Analysis method of the Natural Language API to extract the ingredients and cookware from each recipe Evaluate the model's performance on a prelabeled dataset.

B. Create a text dataset on Vertex Al for entity extraction Create as many entities as there are different ingredients and cookware Train an AutoML entity extraction model to extract those entities Evaluate the models performance on a holdout dataset.

C. Create a text dataset on Vertex Al for entity extraction Create two entities called ingredient" and cookware" and label at least 200 examples of each entity Train an AutoML entity extraction model to extract occurrences of these entity types Evaluate performance on a holdout dataset.

D. Create a multi-label text classification dataset on Vertex Al Create a test dataset and label each recipe that corresponds to its ingredients and cookware Train a multi-class classification model Evaluate the model's performance on a holdout dataset.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제12

You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop Model training will use a large batch size, and you expect training to take several weeks You need to configure a training architecture that minimizes both training time and compute costs What should you do?

A.

B.

C.

D.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

최신 Professional-Machine-Learning-Engineer 무료덤프 - Google Professional Machine Learning Engineer

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