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質問 23
You are an ML engineer at a global shoe store. You manage the ML models for the company’s website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

  • A. Build a collaborative-based filtering model
  • B. Build a regression model using the features as predictors
  • C. Build a knowledge-based filtering model
  • D. Build a classification model

正解: A

解説:
Reference:
https://developers.google.com/machine-learning/recommendation/collaborative/basics
https://cloud.google.com/architecture/recommendations-using-machine-learning-on-compute-engine#filtering_the_data

 

質問 24
You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

  • A. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
  • B. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.
  • C. Rewrite your input function to resize and reshape the input images.
  • D. Rewrite your input function using parallel reads, parallel processing, and prefetch.

正解: A

 

質問 25
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen.
Which combination of algorithms would provide the appropriate insights? (Choose two.)

  • A. The Latent Dirichlet Allocation (LDA) algorithm
  • B. The factorization machines (FM) algorithm
  • C. The principal component analysis (PCA) algorithm
  • D. The k-means algorithm
  • E. The Random Cut Forest (RCF) algorithm

正解: C,D

解説:
Explanation/Reference:
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.

 

質問 26
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 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
  • 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 Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for machine learning

正解: B

解説:
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.

 

質問 27
A data scientist uses an Amazon SageMaker notebook instance to conduct data exploration and analysis. This requires certain Python packages that are not natively available on Amazon SageMaker to be installed on the notebook instance.
How can a machine learning specialist ensure that required packages are automatically available on the notebook instance for the data scientist to use?

  • A. Install AWS Systems Manager Agent on the underlying Amazon EC2 instance and use Systems Manager Automation to execute the package installation commands.
  • B. Create an Amazon SageMaker lifecycle configuration with package installation commands and assign the lifecycle configuration to the notebook instance.
  • C. Create a Jupyter notebook file (.ipynb) with cells containing the package installation commands to execute and place the file under the /etc/init directory of each Amazon SageMaker notebook instance.
  • D. Use the conda package manager from within the Jupyter notebook console to apply the necessary conda packages to the default kernel of the notebook.

正解: C

解説:
Explanation
Explanation/Reference: https://towardsdatascience.com/automating-aws-sagemaker-notebooks-2dec62bc2c84

 

質問 28
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