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NEW QUESTION # 42
Your data science team has requested a system that supports scheduled model retraining, Docker containers, and a service that supports autoscaling and monitoring for online prediction requests. Which platform components should you choose for this system?
- A. Vertex AI Pipelines, Vertex AI Prediction, and Vertex AI Model Monitoring
- B. Cloud Composer, BigQuery ML, and Vertex AI Prediction
- C. Cloud Composer, Vertex AI Training with custom containers, and App Engine
- D. Vertex AI Pipelines and App Engine
Answer: D
NEW QUESTION # 43
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 batch size' parameter
- B. Modify the 'scale-tier' parameter
- C. Modify the 'learning rate' parameter
- D. Modify the 'epochs' parameter
Answer: D
NEW QUESTION # 44
You lead a data science team at a large international corporation. Most of the models your team trains are large-scale models using high-level TensorFlow APIs on AI Platform with GPUs. Your team usually takes a few weeks or months to iterate on a new version of a model. You were recently asked to review your team's spending. How should you reduce your Google Cloud compute costs without impacting the model's performance?
- A. Use AI Platform to run distributed training jobs without checkpoints.
- B. Use AI Platform to run distributed training jobs with checkpoints.
- C. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs without checkpoints.
- D. Migrate to training with Kuberflow on Google Kubernetes Engine, and use preemptible VMs with checkpoints.
Answer: C
NEW QUESTION # 45
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
- B. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
- C. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
- D. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
Answer: A
NEW QUESTION # 46
You have a functioning end-to-end ML pipeline that involves tuning the hyperparameters of your ML model using Al Platform, and then using the best-tuned parameters for training. Hypertuning is taking longer than expected and is delaying the downstream processes. You want to speed up the tuning job without significantly compromising its effectiveness. Which actions should you take?
Choose 2 answers
- A. Decrease the range of floating-point values
- B. Decrease the maximum number of trials during subsequent training phases.
- C. Set the early stopping parameter to TRUE
- D. Decrease the number of parallel trials
- E. Change the search algorithm from Bayesian search to random search.
Answer: B,E
NEW QUESTION # 47
You are designing an ML recommendation model for shoppers on your company's ecommerce website. You will use Recommendations Al to build, test, and deploy your system. How should you develop recommendations that increase revenue while following best practices?
- A. Because it will take time to collect and record product data, use placeholder values for the product catalog to test the viability of the model.
- B. Use the "Frequently Bought Together' recommendation type to increase the shopping cart size for each order.
- C. Use the "Other Products You May Like" recommendation type to increase the click-through rate
- D. Import your user events and then your product catalog to make sure you have the highest quality event stream
Answer: D
NEW QUESTION # 48
You have written unit tests for a Kubeflow Pipeline that require custom libraries. You want to automate the execution of unit tests with each new push to your development branch in Cloud Source Repositories. What should you do?
- A. Write a script that sequentially performs the push to your development branch and executes the unit tests on Cloud Run
- B. Using Cloud Build, set an automated trigger to execute the unit tests when changes are pushed to your development branch.
- C. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories. Execute the unit tests using a Cloud Function that is triggered when messages are sent to the Pub/Sub topic
- D. Set up a Cloud Logging sink to a Pub/Sub topic that captures interactions with Cloud Source Repositories Configure a Pub/Sub trigger for Cloud Run, and execute the unit tests on Cloud Run.
Answer: B
NEW QUESTION # 49
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. Add more data to your test set to ensure that you have a fair distribution and sample for testing
- B. Normalize the data for the training, and test datasets as two separate steps.
- C. Split the training and test data based on time rather than a random split to avoid leakage
- D. Apply data transformations before splitting, and cross-validate to make sure that the transformations are applied to both the training and test sets.
Answer: D
NEW QUESTION # 50
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate. You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern. How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
- A. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
- B. Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.
- C. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
- D. Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.
Answer: B
NEW QUESTION # 51
You are an ML engineer at a global car manufacturer. You need to build an ML model to predict car sales in different cities around the world. Which features or feature crosses should you use to train city-specific relationships between car type and number of sales?
- A. Two feature crosses as a element-wise product the first between binned latitude and one-hot encoded car type, and the second between binned longitude and one-hot encoded car type
- B. One feature obtained as an element-wise product between binned latitude, binned longitude, and one-hot encoded car type
- C. One feature obtained as an element-wise product between latitude, longitude, and car type
- D. Three individual features binned latitude, binned longitude, and one-hot encoded car type
Answer: D
NEW QUESTION # 52
You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?
- A. Use Cloud Composer for the orchestration.
- B. Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
- C. Use Kubeflow Pipelines on Google Kubernetes Engine.
- D. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
Answer: C
NEW QUESTION # 53
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?
- A. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
- B. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
- C. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.
- D. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
Answer: A
NEW QUESTION # 54
A Machine Learning Specialist wants to determine the appropriate
SageMakerVariantInvocationsPerInstancesetting for an endpoint automatic scaling configuration.
The Specialist has performed a load test on a single instance and determined that peak requests per second (RPS) without service degradation is about 20 RPS. As this is the first deployment, the Specialist intends to set the invocation safety factor to 0.5.
Based on the stated parameters and given that the invocations per instance setting is measured on a per- minute basis, what should the Specialist set as the SageMakerVariantInvocationsPerInstance setting?
- A. 0
- B. 1
- C. 2,400
- D. 2
Answer: A
NEW QUESTION # 55
You have been asked to develop an input pipeline for an ML training model that processes images from disparate sources at a low latency. You discover that your input data does not fit in memory. How should you create a dataset following Google-recommended best practices?
- A. Create a tf.data.Dataset.prefetch transformation
- B. Convert the images to tf .Tensor Objects, and then run Dataset. from_tensor_slices{).
- C. Convert the images Into TFRecords, store the images in Cloud Storage, and then use the tf. data API to read the images for training
- D. Convert the images to tf .Tensor Objects, and then run tf. data. Dataset. from_tensors ().
Answer: B
NEW QUESTION # 56
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream.
As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
- A. Add more consumers using the Kinesis Client Library (KCL).
- B. Increase the number of S3 prefixes for the delivery stream to write to.
- C. Decrease the retention period for the data stream.
- D. Increase the number of shards for the data stream.
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION # 57
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?
- A. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
- B. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
- C. Apply a L2 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
- D. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
Answer: C
Explanation:
Applying a L2 regularization parameter of 0.4 and decreasing the learning rate by a factor of 10 can help to reduce overfitting and make the model more resilient. Source: Google Cloud
NEW QUESTION # 58
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?
- A. Compare the mean average precision across the models using the Continuous Evaluation feature
- B. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
- C. Compare the loss performance for each model on the validation data
- D. Compare the loss performance for each model on a held-out dataset.
Answer: C
NEW QUESTION # 59
A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.
Which approach should the Specialist use to continue working?
- A. Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.
- B. Download the SageMaker notebook to their local environment, then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
- C. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
- D. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
Answer: C
Explanation:
Explanation
NEW QUESTION # 60
You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano. Scikit-team, and custom libraries. What should you do?
- A. Use the Al Platform custom containers feature to receive training jobs using any framework
- B. Create a library of VM images on Compute Engine; and publish these images on a centralized repository
- C. Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.
- D. Configure Kubeflow to run on Google Kubernetes Engine and receive training jobs through TFJob
Answer: C
NEW QUESTION # 61
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
- A. Search for duplicate accounts in the AWS Glue Data Catalog.
- B. Create a FindMatches machine learning transform in AWS Glue.
- C. Execute the built-in FindDuplicates Amazon Athena query.
- D. Create an AWS Glue crawler to infer duplicate accounts in the source data.
Answer: B
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
NEW QUESTION # 62
You are developing an ML model intended to classify whether X-Ray images indicate bone fracture risk. You have trained on Api Resnet architecture on Vertex AI using a TPU as an accelerator, however you are unsatisfied with the trainning time and use memory usage. You want to quickly iterate your training code but make minimal changes to the code. You also want to minimize impact on the models accuracy. What should you do?
- A. Configure your model to use bfloat16 instead float32
- B. Reduce the global batch size from 1024 to 256
- C. Reduce the number of layers in the model architecture
- D. Reduce the dimensions of the images used un the model
Answer: B
NEW QUESTION # 63
You work for a large technology company that wants to modernize their contact center. You have been asked to develop a solution to classify incoming calls by product so that requests can be more quickly routed to the correct support team. You have already transcribed the calls using the Speech-to-Text API. You want to minimize data preprocessing and development time. How should you build the model?
- A. Use AutoML Natural Language to extract custom entities for classification
- B. Build a custom model to identify the product keywords from the transcribed calls, and then run the keywords through a classification algorithm
- C. Use the Cloud Natural Language API to extract custom entities for classification
- D. Use the Al Platform Training built-in algorithms to create a custom model
Answer: A
NEW QUESTION # 64
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
- A. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting
- B. Implement an AWS Lambda function to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- C. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
Answer: C
NEW QUESTION # 65
You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK. How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?
- A. Use the predefined components available in the Kubeflow Pipelines SDK to access Dataproc, and run the custom code there.
- B. Package the custom Python code into Docker containers, and use the load_component_from_file function to import the containers into the pipeline.
- C. Deploy the custom Python code to Cloud Functions, and use Kubeflow Pipelines to trigger the Cloud Function.
- D. Use the func_to_container_op function to create custom components from the Python code.
Answer: C
NEW QUESTION # 66
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