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NEW QUESTION 37 You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

• A. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
• B. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning
• C. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
• D. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

Answer: D Explanation: Google has bought this software and support for this tool is not good. SQL can work in Cloud fusion pipelines too but I would prefer to use a single tool like Bigquery to both transform and store data.   NEW QUESTION 38 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. 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.
• B. 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.
• C. 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
• D. 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.

Answer: B   NEW QUESTION 39 A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers. Currently, the company has the following data in Amazon Aurora: * Profiles for all past and existing customers * Profiles for all past and existing insured pets * Policy-level information * Premiums received * Claims paid What steps should be taken to implement a machine learning model to identify potential new customers on social media?

• A. Use clustering on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• B. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.
• C. Use regression on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
• D. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media.

Answer: B   NEW QUESTION 40 A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset. Which tool should be used to improve the validation accuracy?

• A. Amazon Comprehend syntax analysis and entity detection
• B. Scikit-leam term frequency-inverse document frequency (TF-IDF) vectorizer
• C. Amazon SageMaker BlazingText cbowmode
• D. Natural Language Toolkit (NLTK) stemming and stop word removal

Answer: B Explanation: Explanation/Reference: https://monkeylearn.com/sentiment-analysis/   NEW QUESTION 41 ......