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新版Professional-Machine-Learning-Engineer考古題 - Professional-Machine-Learning-Engineer測試

在這個資訊時代，IT行業被很多人關注，但是在如今人才濟濟的社會裏任然比較缺乏IT人。很多公司都招聘IT人才，他們一般考察IT人才的能力會參考他們擁有的IT相關認證證書，所以擁有一些IT相關的認證證書是受很多公司歡迎的。但是這些認證證書也不是很容易就能拿到的。Google Professional-Machine-Learning-Engineer 就是一個相當有難度的認證考試，雖然很多人報名參加Google Professional-Machine-Learning-Engineer考試，但是通過率並不是很高。

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>> 新版Professional-Machine-Learning-Engineer考古題 <<

Professional-Machine-Learning-Engineer測試，Professional-Machine-Learning-Engineer題庫分享

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最新的 Google Cloud Certified Professional-Machine-Learning-Engineer 免費考試真題 (Q18-Q23):

問題 #18
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?

• A. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
• B. Use the Al Platform custom containers feature to receive training jobs using any framework
• C. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
• D. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob

答案：C

問題 #19
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured dat a. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?

• A. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
• B. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Kubeflow Pipelines deployed on Google Kubernetes Engine.
• C. Create the pipeline using TensorFlow Extended (TFX) and standard TFX components. Orchestrate the pipeline using Vertex AI Pipelines.
• D. Create the pipeline using Kubeflow Pipelines domain-specific language (DSL) and predefined Google Cloud components. Orchestrate the pipeline using Vertex AI Pipelines.

答案：C

問題 #20
You are building a predictive maintenance model to preemptively detect part defects in bridges. You plan to use high definition images of the bridges as model inputs. You need to explain the output of the model to the relevant stakeholders so they can take appropriate action. How should you build the model?

• A. Use TensorFlow to create a deep learning-based model and use the sampled Shapley method to explain the model output.
• B. Use TensorFlow to create a deep learning-based model and use Integrated Gradients to explain the model output.
• C. Use scikit-learn to build a tree-based model, and use SHAP values to explain the model output.
• D. Use scikit-lean to build a tree-based model, and use partial dependence plots (PDP) to explain the model output.

答案：B

問題 #21
You need to analyze user activity data from your company's mobile applications. Your team will use BigQuery for data analysis, transformation, and experimentation with ML algorithms. You need to ensure real-time ingestion of the user activity data into BigQuery. What should you do?

• A. Configure Pub/Sub to stream the data into BigQuery.
• B. Run a Dataflow streaming job to ingest the data into BigQuery.
• C. Run an Apache Spark streaming job on Dataproc to ingest the data into BigQuery.
• D. Configure Pub/Sub and a Dataflow streaming job to ingest the data into BigQuery,

答案：A

問題 #22
You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?

• A. Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
• B. Containerize the PySpark transformation step, and add it to your pipeline.
• C. Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.
• D. Remove the data transformation step from your pipeline.

答案：C

問題 #23
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