Professional-Machine-Learning-Engineerの無料問題集（290題）、Professional-Machine-Learning-Engineer認定試験参考書

Google Professional-Machine-Learning-Engineer 問題集

試験コード：Professional-Machine-Learning-Engineer

試験名称：Google Professional Machine Learning Engineer

最近更新時間：2024-12-20

問題と解答：全290問

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質問 1：
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 BigQuery to perform analytics Use a Dataflow pipeline to create the features, and store them in Vertex Al Feature Store for online prediction.
B. Ingest the Avro files into Cloud Spanner to perform analytics. Use a Dataflow pipeline to create the features. and store them in Vertex Al Feature Store for online prediction.
C. 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.
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.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 2：
You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?
A. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
B. Rewrite your input function to resize and reshape the input images.
C. Rewrite your input function using parallel reads, parallel processing, and prefetch.
D. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 3：
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
A. Build a collaborative-based filtering model
B. Build a regression model using the features as predictors
C. Build a knowledge-based filtering model
D. Build a classification model
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
You are an ML engineer at a bank. You have developed a binary classification model using AutoML Tables to predict whether a customer will make loan payments on time. The output is used to approve or reject loan requests. One customer's loan request has been rejected by your model, and the bank's risks department is asking you to provide the reasons that contributed to the model's decision. What should you do?
A. Use local feature importance from the predictions.
B. Use the feature importance percentages in the model evaluation page.
C. Use the correlation with target values in the data summary page.
D. Vary features independently to identify the threshold per feature that changes the classification.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 5：
You work as an analyst at a large banking firm. You are developing a robust, scalable ML pipeline to train several regression and classification models. Your primary focus for the pipeline is model interpretability.
You want to productionize the pipeline as quickly as possible What should you do?
A. Use Tabular Workflow forTabel through Vertex Al Pipelines to train attention-based models.
B. Use Cloud Composer to build the training pipelines for custom deep learning-based models.
C. Use Tabular Workflow for Wide & Deep through Vertex Al Pipelines to jointly train wide linear models and deep neural networks.
D. Use Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 6：
You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?
A. Use the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.
B. Create a Cloud Run function that is triggered when new data arrives in the bucket. The function initiates a Vertex AI Pipeline to preprocess the new data and store the processed features in Vertex AI Feature Store.
C. Build a Dataflow pipeline to preprocess the new data in the bucket and store the processed features in BigQuery. Configure a cron job to trigger the pipeline execution.
D. Create a pipeline using the Vertex AI SDK. Schedule the pipeline with Cloud Scheduler to preprocess the new data in the bucket. Store the processed features in Vertex AI Feature Store.
正解：B
解説: (Topexam メンバーにのみ表示されます)

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Google Professional-Machine-Learning-Engineer 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Collaborating within and across teams to manage data and models: It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter notebooks to model prototype. Lastly, it discusses tracking and running ML experiments.
トピック 2	Serving and scaling models: Serving models and scaling online model serving are its sub-topics.
トピック 3	Architecting low-code ML solutions: It covers development of ML models by using BigQuery ML, using ML APIs to build AI solutions, and using AutoML to train models.
トピック 4	Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
トピック 5	Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting suitable hardware for training.

参照：https://cloud.google.com/certification/guides/machine-learning-engineer

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