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NEW QUESTION 48
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 Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
- B. Use Kubeflow Pipelines on Google Kubernetes Engine.
- C. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
- D. Use Cloud Composer for the orchestration.
Answer: B
NEW QUESTION 49
An employee found a video clip with audio on a company’s social media feed. The language used in the video is Spanish. English is the employee’s first language, and they do not understand Spanish. The employee wants to do a sentiment analysis.
What combination of services is the MOST efficient to accomplish the task?
- A. Amazon Transcribe, Amazon Translate, and Amazon Comprehend
- B. Amazon Transcribe, Amazon Translate and Amazon SageMaker BlazingText
- C. Amazon Transcribe, Amazon Translate, and Amazon SageMaker Neural Topic Model (NTM)
- D. Amazon Transcribe, Amazon Comprehend, and Amazon SageMaker seq2seq
Answer: C
NEW QUESTION 50
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. Run a hyperparameter tuning job on AI Platform using custom containers.
- B. Create a Kuberflow Pipelines instance, and run a hyperparameter tuning job on Katib.
- C. Convert the model to a TensorFlow model, and run a hyperparameter tuning job on AI Platform.
- D. Convert the model to a Keras model, and run a Keras Tuner job.
Answer: B
NEW QUESTION 51
You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible. What should you do?
- A. Deploy the model in a TFServing container on Google Kubernetes Engine, and invoke it in the Dataflow job.
- B. Load the model directly into the Dataflow job as a dependency, and use it for prediction.
- C. Containerize the model prediction logic in Cloud Run, which is invoked by Dataflow.
- D. Deploy the model to a Vertex AI endpoint, and invoke this endpoint in the Dataflow job.
Answer: C
Explanation:
Containerizing the model prediction logic in Cloud Run allows for easy and efficient deployment of the model, and allows it to be invoked by Dataflow. Cloud Run is a fully managed service that allows you to run stateless containers in a serverless environment. It automatically scales instances up and down based on the traffic, which can minimize the serving latency.
Additionally, Dataflow can easily invoke Cloud Run services via HTTP requests, making it simple to integrate into your pipeline. This allows the Dataflow pipeline to focus on data ingestion and processing, while the Cloud Run service handles the real-time predictions.
While it is possible to load the model directly into the Dataflow job as a dependency, this approach can increase the complexity of the pipeline and could lead to increased latency. Other options, such as deploying the model to a Vertex AI endpoint or a TFServing container on GKE, would also work but this option is the most optimal for minimizing the serving latency.
NEW QUESTION 52
A monitoring service generates 1 TB of scale metrics record data every minute. A Research team performs queries on this data using Amazon Athena. The queries run slowly due to the large volume of data, and the team requires better performance.
How should the records be stored in Amazon S3 to improve query performance?
- A. CSV files
- B. RecordIO
- C. Compressed JSON
- D. Parquet files
Answer: D
NEW QUESTION 53
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