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 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

解説: (GoShiken メンバーにのみ表示されます)

出題：2

You recently used XGBoost to train a model in Python that will be used for online serving Your model prediction service will be called by a backend service implemented in Golang running on a Google Kubemetes Engine (GKE) cluster Your model requires pre and postprocessing steps You need to implement the processing steps so that they run at serving time You want to minimize code changes and infrastructure maintenance and deploy your model into production as quickly as possible. What should you do?

A. Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server and deploy it on your organization's GKE cluster.

B. Use the XGBoost prebuilt serving container when importing the trained model into Vertex Al Deploy the model to a Vertex Al endpoint Work with the backend engineers to implement the pre- and postprocessing steps in the Golang backend service.

C. Use the Predictor interface to implement a custom prediction routine Build the custom contain upload the container to Vertex Al Model Registry, and deploy it to a Vertex Al endpoint.

D. Use FastAPI to implement an HTTP server Create a Docker image that runs your HTTP server Upload the image to Vertex Al Model Registry and deploy it to a Vertex Al endpoint.

正解：C 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：3

Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

A. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.

B. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.

C. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.

D. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.

正解：C 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：4

You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

A. Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use MultiWorkerMirroredStrategy to train the model.

B. Configure a v3-8 TPU VM SSH into the VM to tram and debug the model.

C. Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use Parameter Server Strategy to train the model.

D. Configure a v3-8 TPU node Use Cloud Shell to SSH into the Host VM to train and debug the model.

正解：B 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：5

You work at a bank. You need to develop a credit risk model to support loan application decisions You decide to implement the model by using a neural network in TensorFlow Due to regulatory requirements, you need to be able to explain the models predictions based on its features When the model is deployed, you also want to monitor the model's performance overtime You decided to use Vertex Al for both model development and deployment What should you do?

A. Use Vertex Explainable Al with the XRAI method and enable Vertex Al Model Monitoring to check for feature distribution skew.

B. Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution skew.

C. Use Vertex Explainable Al with the sampled Shapley method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

D. Use Vertex Explainable Al with the XRAI method, and enable Vertex Al Model Monitoring to check for feature distribution drift.

正解：C 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：6

You need to quickly build and train a model to predict the sentiment of customer reviews with custom categories without writing code. You do not have enough data to train a model from scratch. The resulting model should have high predictive performance. Which service should you use?

A. AI Platform Training built-in algorithms

B. Cloud Natural Language API

C. AutoML Natural Language

D. AI Hub pre-made Jupyter Notebooks

正解：C 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：7

You work at a mobile gaming startup that creates online multiplayer games Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience. You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated Your model has performed well during testing, and you now need to deploy the model to production You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

A. Import the model into Vertex Al Model Registry Create a Vertex Al endpoint that hosts the model and make online inference requests.

B. Save the model files in a VM Load the model files each time there is a prediction request and run an inference job on the VM.

C. Import the model into Vertex Al Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.

D. Save the model files in a Cloud Storage Bucket Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.

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)

出題：8

Your company needs to generate product summaries for vendors. You evaluated a foundation model from Model Garden for text summarization but found that the summaries do not align with your company's brand voice. How should you improve this LLM-based summarization model to better meet your business objectives?

A. Replace the pre-trained model with another model in Model Garden.

B. Tune the token output limit in the response.

C. Increase the model's temperature parameter.

D. Fine-tune the model using a company-specific dataset.

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：9

You work for a pharmaceutical company based in Canada. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?

A. Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.

B. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.

C. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.

D. Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.

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：10

You are building a TensorFlow text-to-image generative model by using a dataset that contains billions of images with their respective captions. You want to create a low maintenance, automated workflow that reads the data from a Cloud Storage bucket collects statistics, splits the dataset into training/validation/test datasets performs data transformations, trains the model using the training/validation datasets. and validates the model by using the test dataset. What should you do?

A. Use the MLFlow SDK and deploy it on a Google Kubernetes Engine Cluster Create multiple components that use Dataflow and Vertex Al services.

B. Use the Kubeflow Pipelines (KFP) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

C. Use the TensorFlow Extended (TFX) SDK to create multiple components that use Dataflow and Vertex Al services Deploy the workflow on Vertex Al Pipelines.

D. Use the Apache Airflow SDK to create multiple operators that use Dataflow and Vertex Al services Deploy the workflow on Cloud Composer.

正解：C 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：11

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

解説: (GoShiken メンバーにのみ表示されます)

出題：12

You work on a data science team at a bank and are creating an ML model to predict loan default risk. You have collected and cleaned hundreds of millions of records worth of training data in a BigQuery table, and you now want to develop and compare multiple models on this data using TensorFlow and Vertex AI. You want to minimize any bottlenecks during the data ingestion state while considering scalability. What should you do?

A. Use TensorFlow I/O's BigQuery Reader to directly read the data.

B. Export data to CSV files in Cloud Storage, and use tf.data.TextLineDataset() to read them.

C. Use the BigQuery client library to load data into a dataframe, and use tf.data.Dataset.
from_tensor_slices() to read it.

D. Convert the data into TFRecords, and use tf.data.TFRecordDataset() to read them.

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)

出題：13

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：14

You are developing a custom image classification model in Python. You plan to run your training application on Vertex Al Your input dataset contains several hundred thousand small images You need to determine how to store and access the images for training. You want to maximize data throughput and minimize training time while reducing the amount of additional code. What should you do?

A. Store image files in Cloud Storage and access them by using serialized records.

B. Store image files in Cloud Storage and access them directly.

C. Store image files in Cloud Filestore, and access them by using serialized records.

D. Store image files in Cloud Filestore and access them directly by using an NFS mount point.

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)

出題：15

While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

A. Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.

B. Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.

C. Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.

D. Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

正解：C 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：16

You work for a credit card company and have been asked to create a custom fraud detection model based on historical data using AutoML Tables. You need to prioritize detection of fraudulent transactions while minimizing false positives. Which optimization objective should you use when training the model?

A. An optimization objective that maximizes the Precision at a Recall value of 0.50

B. An optimization objective that maximizes the area under the precision-recall curve (AUC PR) value

C. An optimization objective that maximizes the area under the receiver operating characteristic curve (AUC ROC) value

D. An optimization objective that minimizes Log loss

正解：B 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：17

You work for a bank You have been asked to develop an ML model that will support loan application decisions. You need to determine which Vertex Al services to include in the workflow You want to track the model's training parameters and the metrics per training epoch. You plan to compare the performance of each version of the model to determine the best model based on your chosen metrics. Which Vertex Al services should you use?

A. Vertex Al Pipelines. Vertex Al Experiments, and Vertex Al Vizier

B. Vertex ML Metadata Vertex Al Feature Store, and Vertex Al Vizier

C. Vertex ML Metadata Vertex Al Experiments, and Vertex Al TensorBoard

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

正解：C 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：18

You work for a global footwear retailer and need to predict when an item will be out of stock based on historical inventory data. Customer behavior is highly dynamic since footwear demand is influenced by many different factors. You want to serve models that are trained on all available data, but track your performance on specific subsets of data before pushing to production. What is the most streamlined and reliable way to perform this validation?

A. Use the entire dataset and treat the area under the receiver operating characteristics curve (AUC ROC) as the main metric.

B. Use the TFX ModelValidator tools to specify performance metrics for production readiness

C. Use k-fold cross-validation as a validation strategy to ensure that your model is ready for production.

D. Use the last relevant week of data as a validation set to ensure that your model is performing accurately on current data

正解：B 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：19

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. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

C. Rewrite your input function using parallel reads, parallel processing, and prefetch.

D. Rewrite your input function to resize and reshape the input images.

正解：C 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：20

You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?
Choose 2 answers

A. Add more positive examples to the training set.

B. Reduce the maximum number of node hours for training.

C. Decrease the score threshold.

D. Increase the score threshold.

E. Add more negative examples to the training set.

正解：A,C 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」