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 creating a deep neural network classification model using a dataset with categorical input values.
Certain columns have a cardinality greater than 10,000 unique values. How should you encode these categorical values as input into the model?

A. Convert each categorical value into an integer value.

B. Map the categorical variables into a vector of boolean values.

C. Convert each categorical value into a run-length encoded string.

D. Convert the categorical string data to one-hot hash buckets.

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)

出題：2

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. F Score with higher recall weighted than precision

B. F1 Score

C. F Score with higher precision weighting than recall

D. RMSE

正解：B 解答を投票する

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出題：3

You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

A.

B.

正解：B 解答を投票する

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出題：4

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

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

C. 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.

D. 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.

正解：C 解答を投票する

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出題：5

You are an ML engineer on an agricultural research team working on a crop disease detection tool to detect leaf rust spots in images of crops to determine the presence of a disease. These spots, which can vary in shape and size, are correlated to the severity of the disease. You want to develop a solution that predicts the presence and severity of the disease with high accuracy. What should you do?

A. Develop a template matching algorithm using traditional computer vision libraries.

B. Develop an image classification ML model to predict the presence of the disease.

C. Create an object detection model that can localize the rust spots.

D. Develop an image segmentation ML model to locate the boundaries of the rust spots.

正解：D 解答を投票する

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出題：6

You are developing a process for training and running your custom model in production. You need to be able to show lineage for your model and predictions. What should you do?

A. 1 Create a Vertex Al managed dataset
2 Use a Vertex Ai training pipeline to train your model
3 Generate batch predictions in Vertex Al

B. 1 Upload your dataset to BigQuery
2. Use a Vertex Al custom training job to train your model
3 Generate predictions by using Vertex Al SDK custom prediction routines

C. 1 Use Vertex Al Experiments to train your model.
2 Register your model in Vertex Al Model Registry
3. Generate batch predictions in Vertex Al

D. 1 Use a Vertex Al Pipelines custom training job component to train your model
2. Generate predictions by using a Vertex Al Pipelines model batch predict component

正解：C 解答を投票する

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出題：7

You are working with a dataset that contains customer transactions. You need to build an ML model to predict customer purchase behavior You plan to develop the model in BigQuery ML, and export it to Cloud Storage for online prediction You notice that the input data contains a few categorical features, including product category and payment method You want to deploy the model as quickly as possible. What should you do?

A. Use the create model statement and select the categorical and non-categorical features.

B. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

C. Use the transform clause with the ML. ONE_HOT_ENCODER function on the categorical features at model creation and select the categorical and non-categorical features.

D. Use the ML. ONE_HOT_ENCODER function on the categorical features, and select the encoded categorical features and non-categorical features as inputs to create your model.

正解：C 解答を投票する

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出題：8

You have recently developed a custom model for image classification by using a neural network. You need to automatically identify the values for learning rate, number of layers, and kernel size. To do this, you plan to run multiple jobs in parallel to identify the parameters that optimize performance. You want to minimize custom code development and infrastructure management. What should you do?

A. Create a custom training job that uses the Vertex Al Vizier SDK for parameter optimization.

B. Create a Vertex Al hyperparameter tuning job.

C. Train an AutoML image classification model.

D. Create a Vertex Al pipeline that runs different model training jobs in parallel.

正解：B 解答を投票する

出題：9

You recently deployed a model lo a Vertex Al endpoint and set up online serving in Vertex Al Feature Store.
You have configured a daily batch ingestion job to update your featurestore During the batch ingestion jobs you discover that CPU utilization is high in your featurestores online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?

A. Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.

B. Enable autoscaling of the online serving nodes in your featurestore

C. Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

D. Increase the worker counts in the importFeaturevalues request of your batch ingestion job.

正解：B 解答を投票する

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出題：10

You are implementing a batch inference ML pipeline in Google Cloud. The model was developed by using TensorFlow and is stored in SavedModel format in Cloud Storage. You need to apply the model to a historical dataset that is stored in a BigQuery table. You want to perform inference with minimal effort. What should you do?

A. Export the historical data to Cloud Storage in CSV format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

B. Import the TensorFlow model by using the create model statement in BigQuery ML. Apply the historical data to the TensorFlow model.

C. Configure and deploy a Vertex Al endpoint. Use the endpoint to get predictions from the historical data in BigQuery.

D. Export the historical data to Cloud Storage in Avro format. Configure a Vertex Al batch prediction job to generate predictions for the exported data.

正解：D 解答を投票する

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出題：11

You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?

A. Spin up a Vertex Al Workbench user-managed notebooks instance and import the dataset Use this data to create statistical and visual analyses

B. Use BigQuery to calculate the descriptive statistics. Use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses.
D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.

C. Visualize the time plots in Google Data Studio. Import the dataset into Vertex Al Workbench user- managed notebooks Use this data to calculate the descriptive statistics and run the statistical analyses

正解：B 解答を投票する

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出題：12

During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A. Decrease the size of the training batch

B. Decrease the learning rate hyperparameter

C. Increase the learning rate hyperparameter

D. Increase the size of the training batch

正解：B 解答を投票する

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出題：13

You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%.
How can you make your production model more accurate?

A. Normalize the data for the training, and test datasets as two separate steps.

B. Split the training and test data based on time rather than a random split to avoid leakage

C. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.

D. Add more data to your test set to ensure that you have a fair distribution and sample for testing

正解：B 解答を投票する

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出題：14

You work for an organization that operates a streaming music service. You have a custom production model that is serving a "next song" recommendation based on a user's recent listening history. Your model is deployed on a Vertex Al endpoint. You recently retrained the same model by using fresh data. The model received positive test results offline. You now want to test the new model in production while minimizing complexity. What should you do?

A. Create a new Vertex Al endpoint for the new model and deploy the new model to that new endpoint Build a service to randomly send 5% of production traffic to the new endpoint Monitor end-user metrics such as listening time If end-user metrics improve between models over time gradually increase the percentage of production traffic sent to the new endpoint.

B. Configure a model monitoring job for the existing Vertex Al endpoint. Configure the monitoring job to detect prediction drift, and set a threshold for alerts Update the model on the endpoint from the previous model to the new model If you receive an alert of prediction drift, revert to the previous model.

C. Capture incoming prediction requests in BigQuery Create an experiment in Vertex Al Experiments Run batch predictions for both models using the captured data Use the user's selected song to compare the models performance side by side If the new models performance metrics are better than the previous model deploy the new model to production.

D. Deploy the new model to the existing Vertex Al endpoint Use traffic splitting to send 5% of production traffic to the new model Monitor end-user metrics, such as listening time If end-user metrics improve between models over time, gradually increase the percentage of production traffic sent to the new model.

正解：D 解答を投票する

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出題：15

You built a deep learning-based image classification model by using on-premises data. You want to use Vertex Al to deploy the model to production Due to security concerns you cannot move your data to the cloud. You are aware that the input data distribution might change over time You need to detect model performance changes in production. What should you do?

A. Use Vertex Explainable Al for model explainability Configure feature-based explanations.

B. Use Vertex Explainable Al for model explainability Configure example-based explanations.

C. Create a Vertex Al Model Monitoring job. Enable feature attribution skew and dnft detection for your model.

D. Create a Vertex Al Model Monitoring job. Enable training-serving skew detection for your model.

正解：D 解答を投票する

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出題：16

You are building an ML model to predict trends in the stock market based on a wide range of factors. While exploring the data, you notice that some features have a large range. You want to ensure that the features with the largest magnitude don't overfit the model. What should you do?

A. Standardize the data by transforming it with a logarithmic function.

B. Apply a principal component analysis (PCA) to minimize the effect of any particular feature.

C. Normalize the data by scaling it to have values between 0 and 1.

D. Use a binning strategy to replace the magnitude of each feature with the appropriate bin number.

正解：C 解答を投票する

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出題：17

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

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」