Professional-Machine-Learning-Engineer試験無料問題集（290題）「Google Professional Machine Learning Engineer 認定」

出題：1

You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository

B. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job

C. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code

D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

正解：A 解答を投票する

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出題：2

You are working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?

A. Address the model overfitting by using a less complex algorithm.

B. Address the model overfitting by tuning the hyperparameters to reduce the AUC ROC value.

C. Address data leakage by removing features highly correlated with the target value.

D. Address data leakage by applying nested cross-validation during model training.

正解：D 解答を投票する

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出題：3

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

A. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

B. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.

C. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

D. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler

正解：B 解答を投票する

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出題：4

You are an AI engineer working for a popular video streaming platform. You built a classification model using PyTorch to predict customer churn. Each week, the customer retention team plans to contact customers identified as at-risk for churning with personalized offers. You want to deploy the model while minimizing maintenance effort. What should you do?

A. Deploy the model to a Vertex AI endpoint, and configure the model for batch prediction. Schedule the batch prediction to run weekly.

B. Use Vertex AI's prebuilt containers for prediction. Deploy the model on Google Kubernetes Engine (GKE), and configure the model for batch prediction.

C. Use Vertex AI's prebuilt containers for prediction. Deploy the container on Cloud Run to generate online predictions.

D. Deploy the model to a Vertex AI endpoint, and configure the model for online prediction. Schedule a job to query this endpoint weekly.

正解：A 解答を投票する

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出題：5

You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?

A. Build an AutoML tabular regression model Configure the model to generate explanations when it makes predictions.

B. Build a custom TensorFlow neural network by using Vertex Al custom training Configure the model to generate explanations when it makes predictions.

C. Build a random forest classification model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.

D. Build a random forest regression model in a Vertex Al Workbench notebook instance Configure the model to generate feature importance's after the model is trained.

正解：C 解答を投票する

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出題：6

You are training an object detection machine learning model on a dataset that consists of three million X-ray images, each roughly 2 GB in size. You are using Vertex AI Training to run a custom training application on a Compute Engine instance with 32-cores, 128 GB of RAM, and 1 NVIDIA P100 GPU. You notice that model training is taking a very long time. You want to decrease training time without sacrificing model performance. What should you do?

A. Enable early stopping in your Vertex AI Training job.

B. Use the tf.distribute.Strategy API and run a distributed training job.

C. Increase the instance memory to 512 GB and increase the batch size.

D. Replace the NVIDIA P100 GPU with a v3-32 TPU in the training job.

正解：B 解答を投票する

出題：7

You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

A. Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.

B. Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

C. Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.

D. Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri environment variable to read the images by using the tf. data. Dataset. Iist_flies function.

正解：B 解答を投票する

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出題：8

You are training models in Vertex Al by using data that spans across multiple Google Cloud Projects You need to find track, and compare the performance of the different versions of your models Which Google Cloud services should you include in your ML workflow?

A. Dataplex. Vertex Al Experiments, and Vertex Al ML Metadata

B. Dataplex. Vertex Al Feature Store and Vertex Al TensorBoard

C. Vertex Al Pipelines: Vertex Al Experiments and Vertex Al Metadata

D. Vertex Al Pipelines, Vertex Al Feature Store, and Vertex Al Experiments

正解：D 解答を投票する

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出題：9

You developed an ML model with Al Platform, and you want to move it to production. You serve a few thousand queries per second and are experiencing latency issues. Incoming requests are served by a load balancer that distributes them across multiple Kubeflow CPU-only pods running on Google Kubernetes Engine (GKE). Your goal is to improve the serving latency without changing the underlying infrastructure.
What should you do?

A. Switch to the tensorflow-model-server-universal version of TensorFlow Serving

B. Significantly increase the max_batch_size TensorFlow Serving parameter

C. Significantly increase the max_enqueued_batches TensorFlow Serving parameter

D. Recompile TensorFlow Serving using the source to support CPU-specific optimizations Instruct GKE to choose an appropriate baseline minimum CPU platform for serving nodes

正解：D 解答を投票する

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出題：10

You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

A. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

B. Use Vertex Al Platform for distributed training

C. Create a Managed Instance Group with autoscaling

D. Create a cluster on Dataproc for training

正解：B 解答を投票する

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出題：11

You recently joined a machine learning team that will soon release a new project. As a lead on the project, you are asked to determine the production readiness of the ML components. The team has already tested features and data, model development, and infrastructure. Which additional readiness check should you recommend to the team?

A. Ensure that all hyperparameters are tuned

B. Ensure that model performance is monitored

C. Ensure that feature expectations are captured in the schema

D. Ensure that training is reproducible

正解：B 解答を投票する

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出題：12

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 Google Kubernetes Engine to build a custom training pipeline for XGBoost-based models.

B. Use Cloud Composer to build the training pipelines for custom deep learning-based models.

C. Use Tabular Workflow forTabel through Vertex Al Pipelines to train attention-based models.

D. Use Tabular Workflow for Wide & Deep through Vertex Al Pipelines to jointly train wide linear models and deep neural networks.

正解：B 解答を投票する

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出題：13

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 classification model

C. Build a regression model using the features as predictors

D. Build a knowledge-based filtering model

正解：A 解答を投票する

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出題：14

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：15

You recently created a new Google Cloud Project After testing that you can submit a Vertex Al Pipeline job from the Cloud Shell, you want to use a Vertex Al Workbench user-managed notebook instance to run your code from that instance You created the instance and ran the code but this time the job fails with an insufficient permissions error. What should you do?

A. Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Notebooks Runner role.

B. Ensure that the Vertex Al Workbench instance is on the same subnetwork of the Vertex Al Pipeline resources that you will use.

C. Ensure that the Workbench instance that you created is in the same region of the Vertex Al Pipelines resources you will use.

D. Ensure that the Vertex Al Workbench instance is assigned the Identity and Access Management (1AM) Vertex Al User rote.

正解：D 解答を投票する

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出題：16

You need to develop a custom TensorRow model that will be used for online predictions. The training data is stored in BigQuery. You need to apply instance-level data transformations to the data for model training and serving. You want to use the same preprocessing routine during model training and serving. How should you configure the preprocessing routine?

A. Create a preprocessing function that reads and transforms the data from BigQuery Create a Vertex Al custom prediction routine that calls the preprocessing function at serving time.

B. Create an Apache Beam pipeline to read the data from BigQuery and preprocess it by using TensorFlow Transform and Dataflow.

C. Create a BigQuery script to preprocess the data, and write the result to another BigQuery table.

D. Create a pipeline in Vertex Al Pipelines to read the data from BigQuery and preprocess it using a custom preprocessing component.

正解：B 解答を投票する

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出題：17

One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this. What should you do?

A. Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don't match the schema with 0.

B. Use TensorFlow Data Validation to detect and flag schema anomalies.

C. Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.

D. Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.

正解：B 解答を投票する

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出題：18

You are the lead ML engineer on a mission-critical project that involves analyzing massive datasets using Apache Spark. You need to establish a robust environment that allows your team to rapidly prototype Spark models using Jupyter notebooks. What is the fastest way to achieve this?

A. Use Colab Enterprise with a Spark kernel.

B. Configure a Compute Engine instance with Spark and use Jupyter notebooks.

C. Set up a Vertex AI Workbench instance with a Spark kernel.

D. Set up a Dataproc cluster with Spark and use Jupyter notebooks.

正解：D 解答を投票する

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