최신 Professional-Machine-Learning-Engineer 무료덤프 - Google Professional Machine Learning Engineer

문제1

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

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

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

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

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제2

You recently developed a deep learning model using Keras, and now you are experimenting with different training strategies. First, you trained the model using a single GPU, but the training process was too slow.
Next, you distributed the training across 4 GPUs using tf.distribute.MirroredStrategy (with no other changes), but you did not observe a decrease in training time. What should you do?

A. Use a TPU with tf.distribute.TPUStrategy.

B. Increase the batch size.

C. Create a custom training loop.

D. Distribute the dataset with tf.distribute.Strategy.experimental_distribute_dataset

정답: B

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문제3

You developed a custom model by using Vertex Al to forecast the sales of your company s products based on historical transactional data You anticipate changes in the feature distributions and the correlations between the features in the near future You also expect to receive a large volume of prediction requests You plan to use Vertex Al Model Monitoring for drift detection and you want to minimize the cost. What should you do?

A. Use the features and the feature attributions for monitoring. Set a monitoring-frequency value that is lower than the default.

B. Use the features and the feature attributions for monitoring Set a prediction-sampling-rate value that is closer to 0 than 1.

C. Use the features for monitoring Set a monitoring- frequency value that is higher than the default.

D. Use the features for monitoring Set a prediction-sampling-rare value that is closer to 1 than 0.

정답: B

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문제4

You work for a magazine publisher and have been tasked with predicting whether customers will cancel their annual subscription. In your exploratory data analysis, you find that 90% of individuals renew their subscription every year, and only 10% of individuals cancel their subscription. After training a NN Classifier, your model predicts those who cancel their subscription with 99% accuracy and predicts those who renew their subscription with 82% accuracy. How should you interpret these results?

A. This is not a good result because the model should have a higher accuracy for those who renew their subscription than for those who cancel their subscription.

B. This is not a good result because the model is performing worse than predicting that people will always renew their subscription.

C. This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.

D. This is a good result because the accuracy across both groups is greater than 80%.

정답: B

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문제5

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 feature expectations are captured in the schema

C. Ensure that model performance is monitored

D. Ensure that training is reproducible

정답: C

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문제6

You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:
* Customer_id
* Product_id
* Date
* Days_since_last_purchase (measured in days)
* Average_purchase_frequency (measured in 1/days)
* Purchase (binary class, if customer purchased product on the Date)
You need to interpret your models results for each individual prediction. What should you do?

A. Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.

B. Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.

C. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.

D. Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제7

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

정답: B

설명: (DumpTOP 회원만 볼 수 있음)

문제8

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. 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.

B. 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.

C. Use the Vertex AI SDK to preprocess the new data in the bucket prior to each model retraining. Store the processed features in BigQuery.

D. 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.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제9

You work at a bank You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex Al Model serving container which accepts a string as input for each prediction instance. In each string the feature values are separated by commas. You want to deploy this model to production for online predictions, and monitor the feature distribution over time with minimal effort What should you do?

A. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Ai endpoint.
2. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema.

B. 1 Refactor the serving container to accept key-value pairs as input format.
2 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective.

C. 1 Refactor the serving container to accept key-value pairs as input format.
2. Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective.

D. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
2 Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective and provide an instance schema.

정답: A

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문제10

You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?

A. Modify the 'epochs' parameter

B. Modify the 'learning rate' parameter

C. Modify the batch size' parameter

D. Modify the 'scale-tier' parameter

정답: D

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문제11

Your data science team is training a PyTorch model for image classification based on a pre-trained RestNet model. You need to perform hyperparameter tuning to optimize for several parameters. What should you do?

A. Convert the model to a Keras model, and run a Keras Tuner job.

B. Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.

C. Run a hyperparameter tuning job on AI Platform using custom containers.

D. Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제12

You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to train your model?

A. Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use bfloat16 quantization during model training.

B. Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM.
Use float32 precision during model training.

C. Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM.Use float16 quantization during model training.

D. Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use float32 precision during model training.

정답: D

설명: (DumpTOP 회원만 볼 수 있음)

문제13

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

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

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

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

정답: C

설명: (DumpTOP 회원만 볼 수 있음)

문제14

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 Filestore, and access them by using serialized records.

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

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

정답: A

설명: (DumpTOP 회원만 볼 수 있음)

문제15

Your team needs to build a model that predicts whether images contain a driver's license, passport, or credit card. The data engineering team already built the pipeline and generated a dataset composed of 10,000 images with driver's licenses, 1,000 images with passports, and 1,000 images with credit cards. You now have to train a model with the following label map: ['driversjicense', 'passport', 'credit_card']. Which loss function should you use?

A. Categorical cross-entropy

B. Categorical hinge

C. Sparse categorical cross-entropy

D. Binary cross-entropy

정답: A

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문제16

You are building an ML model to detect anomalies in real-time sensor data. You will use Pub/Sub to handle incoming requests. You want to store the results for analytics and visualization. How should you configure the pipeline?

A. 1 = Dataflow, 2 - Al Platform, 3 = BigQuery

B. 1 = DataProc, 2 = AutoML, 3 = Cloud Bigtable

C. 1 = BigQuery, 2 = AutoML, 3 = Cloud Functions

D. 1 = BigQuery, 2 = Al Platform, 3 = Cloud Storage

정답: A

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문제17

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 Upload the image to Vertex Al Model Registry and deploy it to a Vertex Al endpoint.

B. 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.

C. 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.

D. 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.

정답: B

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문제18

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

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

정답: C

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문제19

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 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

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 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

정답: B

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최신 Professional-Machine-Learning-Engineer 무료덤프 - Google Professional Machine Learning Engineer

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