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Google Professional Machine Learning Engineer Sample Questions (Q115-Q120):

NEW QUESTION # 115
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 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
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 Create a Vertex Al managed dataset
2 Use a Vertex Ai training pipeline to train your model
3 Generate batch predictions in Vertex Al

Answer: C

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "track the lineage of pipeline artifacts". Vertex AI Experiments2 is a service that allows you to track and compare the results of your model training runs. Vertex AI Experiments automatically logs metadata such as hyperparameters, metrics, and artifacts for each training run. You can use Vertex AI Experiments to train your custom model using TensorFlow, PyTorch, XGBoost, or scikit-learn. Vertex AI Model Registry3 is a service that allows you to manage your trained models in a central location. You can use Vertex AI Model Registry to register your model, add labels and descriptions, and view the model's lineage graph. The lineage graph shows the artifacts and executions that are part of the model's creation, such as the dataset, the training pipeline, and the evaluation metrics. The other options are not relevant or optimal for this scenario. References:
* Professional ML Engineer Exam Guide
* Vertex AI Experiments
* Vertex AI Model Registry
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 116
You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?

A. Too much data representing congested areas was used for model training.
B. The model is overfitting in areas with less traffic and underfitting in areas with more traffic.
C. AUC is not the correct metric to evaluate this classification model.
D. Gradients become small and vanish while backpropagating from the output to input nodes.

Answer: D

NEW QUESTION # 117
You have deployed a model on Vertex AI for real-time inference. During an online prediction request, you get an "Out of Memory" error. What should you do?

A. Use base64 to encode your data before using it for prediction.
B. Send the request again with a smaller batch of instances.
C. Use batch prediction mode instead of online mode.
D. Apply for a quota increase for the number of prediction requests.

Answer: B

Explanation:
* Option A is incorrect because using batch prediction mode instead of online mode does not solve the
"Out of Memory" error, but rather changes the latency and throughput of the prediction service. Batch prediction mode is suitable for large-scale, asynchronous, and non-urgent predictions, while online prediction mode is suitable for low-latency, synchronous, and real-time predictions1.
* Option B is correct because sending the request again with a smaller batch of instances can reduce the memory consumption of the prediction service and avoid the "Out of Memory" error. The batch size is the number of instances that are processed together in one request. A smaller batch size means less data to load into memory at once2.
* Option C is incorrect because using base64 to encode your data before using it for prediction does not reduce the memory consumption of the prediction service, but rather increases it. Base64 encoding is a way of representing binary data as ASCII characters, which increases the size of the data by about
33%3. Base64 encoding is only required for certain data types, such as images and audio, that cannot be represented as JSON or CSV4.
* Option D is incorrect because applying for a quota increase for the number of prediction requests does not solve the "Out of Memory" error, but rather increases the number of requests that can be sent to the
* prediction service per day. Quotas are limits on the usage of Google Cloud resources, such as CPU, memory, disk, and network5. Quotas do not affect the performance of the prediction service, but rather the availability and cost of the service.
References:
* Choosing between online and batch prediction
* Online prediction input data
* Base64 encoding
* Preparing data for prediction
* Quotas and limits

NEW QUESTION # 118
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. 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.
B. Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.
C. 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.
D. Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

Answer: D

NEW QUESTION # 119
You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

A. data = json dumps({"signature_name": f,serving_default", "instances": [['a', 'b'], [c 'd'], ['e T]]})
B. data = json.dumps({"signature_name": "serving_default, "instances": [['a', 'b 'c'1, [d 'e T]]})
C. data = json dumps({"signature_name": "serving_default"! "instances": [['a', 'b', "c", 'd', 'e', 'f']]})
D. data = json.dumps({"signature_name": "serving_default' "instances": [fab', 'be1, 'cd']]})

Answer: C

NEW QUESTION # 120
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Professional-Machine-Learning-Engineer Learning Engine | Professional-Machine-Learning-Engineer Latest Exam Pdf

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How much Professional Machine Learning Engineer - Google Cost

Google Professional Machine Learning Engineer Sample Questions (Q115-Q120):

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