Professional-Machine-Learning-Engineer試験無料問題集（285題）「Google Professional Machine Learning Engineer 認定」

出題：1

You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects. Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

A. AutoML Vision model

B. AutoML Vision Edge mobile-low-latency-1 model

C. AutoML Vision Edge mobile-high-accuracy-1 model

D. AutoML Vision Edge mobile-versatile-1 model

正解：B 解答を投票する

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出題：2

You are going to train a DNN regression model with Keras APIs using this code:

How many trainable weights does your model have? (The arithmetic below is correct.)

A. 500*256+256*128+128*2 161024

B. 501*256+257*128+128*2161408

C. 500*256*0 25+256*128*0 25+128*2 40448

D. 501*256+257*128+2 161154

正解：A 解答を投票する

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出題：3

Your team is training a large number of ML models that use different algorithms, parameters and datasets. Some models are trained in Vertex Ai Pipelines, and some are trained on Vertex Al Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics What should you do?

A. Create a Vertex Al experiment Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex Al SDK.

B. Implement all models in Vertex Al Pipelines Create a Vertex Al experiment, and associate all pipeline runs with that experiment.

C. Implement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.

D. Store all model parameters and metrics as mode! metadata by using the Vertex Al Metadata API.

正解：A 解答を投票する

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出題：4

You are deploying a new version of a model to a production Vertex Al endpoint that is serving traffic You plan to direct all user traffic to the new model You need to deploy the model with minimal disruption to your application What should you do?

A. 1 Create a new model Set the parentModel parameter to the model ID of the currently deployed model Upload the model to Vertex Al Model Registry.
2 Deploy the new model to the existing endpoint and set the new model to 100% of the traffic.

B. 1, Create a new model Set it as the default version Upload the model to Vertex Al Model Registry
2 Deploy the new model to the existing endpoint

C. 1. Create a new endpoint.
2. Create a new model Set the parentModel parameter to the model ID of the currently deployed model and set it as the default version Upload the model to Vertex Al Model Registry
3. Deploy the new model to the new endpoint and set the new model to 100% of the traffic

D. 1 Create a new endpoint.
2 Create a new model Set it as the default version Upload the model to Vertex Al Model Registry.
3. Deploy the new model to the new endpoint.
4 Update Cloud DNS to point to the new endpoint

正解：A 解答を投票する

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出題：5

You have deployed a scikit-learn model to a Vertex Al endpoint using a custom model server. You enabled auto scaling; however, the deployed model fails to scale beyond one replica, which led to dropped requests. You notice that CPU utilization remains low even during periods of high load. What should you do?

A. Increase the number of workers in your model server.

B. Attach a GPU to the prediction nodes.

C. Schedule scaling of the nodes to match expected demand.

D. Increase the minReplicaCount in your DeployedModel configuration.

正解：A 解答を投票する

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出題：6

Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

A. 1 Cloud Function, 2 Cloud SQL

B. 1 Pub/Sub, 2 Datastore

C. 1 Dataflow, 2 BigQuery

D. 1 Dataflow, 2 Cloud SQL

正解：C 解答を投票する

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出題：7

You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

A. Build your custom container to run jobs on Al Platform Training

B. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training

C. Use a built-in model available on Al Platform Training

D. Build your custom containers to run distributed training jobs on Al Platform Training

正解：D 解答を投票する

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出題：8

You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?

A. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.

B. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.

C. Use AutoML to optimize the model's recall in order to minimize false negatives.

D. Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.

正解：C 解答を投票する

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出題：9

You work for a company that is developing a new video streaming platform. You have been asked to create a recommendation system that will suggest the next video for a user to watch. After a review by an AI Ethics team, you are approved to start development. Each video asset in your company's catalog has useful metadata (e.g., content type, release date, country), but you do not have any historical user event dat a. How should you build the recommendation system for the first version of the product?

A. Launch the product without machine learning. Use simple heuristics based on content metadata to recommend similar videos to users, and start collecting user event data so you can develop a recommender model in the future.

B. Launch the product without machine learning. Present videos to users alphabetically, and start collecting user event data so you can develop a recommender model in the future.

C. Launch the product with machine learning. Generate embeddings for each video by training an autoencoder on the content metadata using TensorFlow. Cluster content based on the similarity of these embeddings, and then recommend videos from the same cluster.

D. Launch the product with machine learning. Use a publicly available dataset such as MovieLens to train a model using the Recommendations AI, and then apply this trained model to your data.

正解：A 解答を投票する

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出題：10

You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. What should you do first?

A. Implement a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Train a model to predict anomalies based on this labeled dataset.

B. Hire a team of qualified analysts to review and label the machines' historical performance data. Train a model based on this manually labeled dataset.

C. Train a time-series model to predict the machines' performance values. Configure an alert if a machine's actual performance values significantly differ from the predicted performance values.

D. Develop a simple heuristic (e.g., based on z-score) to label the machines' historical performance data. Test this heuristic in a production environment.

正解：A 解答を投票する

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出題：11

You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

A. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10

B. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.

C. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

D. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

正解：C 解答を投票する

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出題：12

You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?

A. 1. Create an instance of the CustomJob class with the Vertex AI SDK to train your model.
2. Use the Metadata API to register your model as a model artifact.
3. Using the Notebooks API, create a scheduled execution to run the training code weekly.

B. 1. Create a managed pipeline in Vertex Al Pipelines to train your model using a Vertex Al HyperParameterTuningJobRunOp component.
2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry.
3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

C. 1. Create an instance of the CustomTrainingJob class with the Vertex AI SDK to train your model.
2. Using the Notebooks API, create a scheduled execution to run the training code weekly.

D. 1. Create a managed pipeline in Vertex Al Pipelines to train your model by using a Vertex Al CustomTrainingJoOp component.
2. Use the ModelUploadOp component to upload your model to Vertex Al Model Registry.
3. Use Cloud Scheduler and Cloud Functions to run the Vertex Al pipeline weekly.

正解：D 解答を投票する

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出題：13

You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way. Which strategy should you choose?

A. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.

B. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using

C. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.

D. Use labels to organize resources into descriptive categories. Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.

正解：D 解答を投票する

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出題：14

You developed a BigQuery ML linear regressor model by using a training dataset stored in a BigQuery table. New data is added to the table every minute. You are using Cloud Scheduler and Vertex Al Pipelines to automate hourly model training, and use the model for direct inference. The feature preprocessing logic includes quantile bucketization and MinMax scaling on data received in the last hour. You want to minimize storage and computational overhead. What should you do?

A. Create a component in the Vertex Al Pipelines directed acyclic graph (DAG) to calculate the required statistics, and pass the statistics on to subsequent components.

B. Create SQL queries to calculate and store the required statistics in separate BigQuery tables that are referenced in the CREATE MODEL statement.

C. Preprocess and stage the data in BigQuery prior to feeding it to the model during training and inference.

D. Use the TRANSFORM clause in the CREATE MODEL statement in the SQL query to calculate the required statistics.

正解：D 解答を投票する

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出題：15

You are an AI architect at a popular photo-sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?

A. Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy it to a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.

B. Train an image clustering model using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.

C. Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.

D. Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.

正解：D 解答を投票する

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出題：16

You are training an ML model on a large dataset. You are using a TPU to accelerate the training process You notice that the training process is taking longer than expected. You discover that the TPU is not reaching its full capacity. What should you do?

A. Increase the number of epochs

B. Increase the batch size

C. Increase the learning rate

D. Decrease the learning rate

正解：B 解答を投票する

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出題：17

You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.

B. Use Dataflow with the SavedModel to read the data from BigQuery

C. Export the model to BigQuery ML.

D. Deploy and version the model on Al Platform.

正解：A 解答を投票する

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出題：18

You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?

A. Install the NLTK library from a Jupyter cell by using the! pip install nltk -user command.

B. Write a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.

C. Create a new Vertex Al Workbench notebook with a custom image that includes the NLTK library.

D. Install the NLTK library from a terminal by using the pip install nltk command.

正解：A 解答を投票する

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出題：19

You have recently trained a scikit-learn model that you plan to deploy on Vertex Al. This model will support both online and batch prediction. You need to preprocess input data for model inference. You want to package the model for deployment while minimizing additional code What should you do?

A. 1 Upload your model to the Vertex Al Model Registry by using a prebuilt scikit-learn prediction container
2 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig.inscanceType setting to transform your input data

B. 1 Create a custom container for your sci-kit learn model.
2 Upload your model and custom container to Vertex Al Model Registry
3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job that uses the instanceConfig. instanceType setting to transform your input data

C. 1. Create a custom container for your sci-kit learn model,
2 Define a custom serving function for your model
3 Upload your model and custom container to Vertex Al Model Registry
4 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job

D. 1 Wrap your model in a custom prediction routine (CPR). and build a container image from the CPR local model
2 Upload your sci-kit learn model container to Vertex Al Model Registry
3 Deploy your model to Vertex Al Endpoints, and create a Vertex Al batch prediction job

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

Professional-Machine-Learning-Engineer試験無料問題集「Google Professional Machine Learning Engineer 認定」