QUESTION: 141

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?

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.
This is not a good result because the model is performing worse than predicting that people will always renew their subscription.
This is a good result because predicting those who cancel their subscription is more difficult, since there is less data for this group.
This is a good result because the accuracy across both groups is greater than 80%.

Answer(s): C

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QUESTION: 142

You have built a model that is trained on data stored in Parquet files. You access the data through a Hive table hosted on Google Cloud. You preprocessed these data with PySpark and exported it as a CSV file into Cloud Storage. After preprocessing, you execute additional steps to train and evaluate your model. You want to parametrize this model training in Kubeflow Pipelines. What should you do?

Remove the data transformation step from your pipeline.
Containerize the PySpark transformation step, and add it to your pipeline.
Add a ContainerOp to your pipeline that spins a Dataproc cluster, runs a transformation, and then saves the transformed data in Cloud Storage.
Deploy Apache Spark at a separate node pool in a Google Kubernetes Engine cluster. Add a ContainerOp to your pipeline that invokes a corresponding transformation job for this Spark instance.

Answer(s): D

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QUESTION: 143

You have developed an ML model to detect the sentiment of users’ posts on your company's social media page to identify outages or bugs. You are using Dataflow to provide real-time predictions on data ingested from Pub/Sub. You plan to have multiple training iterations for your model and keep the latest two versions live after every run. You want to split the traffic between the versions in an 80:20 ratio, with the newest model getting the majority of the traffic. You want to keep the pipeline as simple as possible, with minimal management required. What should you do?

Deploy the models to a Vertex AI endpoint using the traffic-split=0=80, PREVIOUS_MODEL_ID=20 configuration.
Wrap the models inside an App Engine application using the --splits PREVIOUS_VERSION=0.2, NEW_VERSION=0.8 configuration
Wrap the models inside a Cloud Run container using the REVISION1=20, REVISION2=80 revision configuration.
Implement random splitting in Dataflow using beam.Partition() with a partition function calling a Vertex AI endpoint.

Answer(s): A

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QUESTION: 144

You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images. You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do?

Create a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs. Prepare and submit a TFJob operator to this node pool.
Create a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and use it to train your model.
Package your code with Setuptools, and use a pre-built container. Train your model with Vertex AI using a custom tier that contains the required GPUs.
Configure a Compute Engine VM with all the dependencies that launches the training. Train your model with Vertex AI using a custom tier that contains the required GPUs.

Answer(s): D

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Free Professional Machine Learning Engineer Exam Braindumps (page: 36)

QUESTION: 141

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QUESTION: 144