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NEW QUESTION 40
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?
Answer: C
NEW QUESTION 41
You work on the data science team at a manufacturing company. You are reviewing the company's historical sales data, which has hundreds of millions of records. For your exploratory data analysis, you need to calculate descriptive statistics such as mean, median, and mode; conduct complex statistical tests for hypothesis testing; and plot variations of the features over time You want to use as much of the sales data as possible in your analyses while minimizing computational resources. What should you do?
Answer: A
Explanation:
D Use BigQuery to calculate the descriptive statistics, and use Google Data Studio to visualize the time plots. Use Vertex Al Workbench user-managed notebooks to run the statistical analyses.
Explanation:
BigQuery is a powerful tool for analyzing large datasets and can be used to quickly calculate descriptive statistics, such as mean, median, and mode, on large amounts of data. By using BigQuery, you can analyze the entire dataset and minimize the computational resources required for your analyses.
Once you have calculated the descriptive statistics, you can use Vertex Al Workbench user-managed notebooks to visualize the time plots and run the statistical analyses. Vertex Al Workbench allows you to interactively explore the data, create visualizations, and perform advanced statistical analysis. It's also possible to run these notebooks on a powerful GPU which will help to increase the speed of the analysis.
NEW QUESTION 42
You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?
* Create a view to abstract your preprocessing logic.
* Query the view every second for new records
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue.B. Validate the accuracy of the model that you trained on preprocessed data
* Create a new model that uses the raw data and is available in real time
* Deploy the new model onto Al Platform for online predictionC. Send incoming prediction requests to a Pub/Sub topic
* Transform the incoming data using a Dataflow job
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queueD. Send incoming prediction requests to a Pub/Sub topic
* Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic.
* Implement your preprocessing logic in the Cloud Function
* Submit a prediction request to Al Platform using the transformed data
* Write the predictions to an outbound Pub/Sub queue
Answer: D
NEW QUESTION 43
You started working on a classification problem with time series data and achieved an area under the receiver operating characteristic curve (AUC ROC) value of 99% for training data after just a few experiments. You haven't explored using any sophisticated algorithms or spent any time on hyperparameter tuning. What should your next step be to identify and fix the problem?
Answer: C
NEW QUESTION 44
You trained a text classification model. You have the following SignatureDefs:
What is the correct way to write the predict request?
Answer: C
Explanation:
https://stackoverflow.com/questions/37956197/what-is-the-negative-index-in-shape-arrays-used-for-tensorflow
NEW QUESTION 45
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