Exam Professional Machine Learning Engineer topic 1 question 93 discussion

It is a data science request that could be ended on Jupiter notebook

More Flexbility

More Flexbility

Max flexibility

Looker studio is good too but it does not give the same depth in statistical analysis of the data as using matplotlib, seaborn etc gives on a notebook. So Jupyterlab notebook a.k.a Vertex AI workbench for me

A as it is a one off report with maximum flexibility. Dont need a dashboard unless being reused

A for more sophisticated statistical analyses and maximum flexibility

A (AI workbench): "sophisticated"

The answer is A. B is wrong because you need more sophisticated statistical analyses and maximum flexibility to create your report.

1. one- time 2. flexibility go for A

went with A, because of max. flexibility

Correct Answer A . While Google Data Studio (Option B) is a powerful data visualization and reporting tool, it might not provide the same level of flexibility and sophistication for statistical analyses compared to a notebook environment.

TensorFlow Data Validation(TFDV) can compute descriptive statistics that provide a quick overview of the data in terms of the features that are present and the shapes of their value distributions. Tools such as Facets Overview can provide a succinct visualization of these statistics for easy browsing.

A. Flexibility is the key.

I think has to be B. One of the keys is that it says quickly and BQ makes it very easy to export the query into Looker Studio. The other one is that there's maximum flexibility within the needs for this case (informative visualizations + statistical analysis), as we can develop and write custom formulas. A feels like overkill to use a Deep Learning VM Image to only describe data and perform some analysis. C also feels overkill to start developping a neural net for that. D although you may use Dataprep for this, it is less suited than A

A seçeneğini öneriyorum çünkü Vertex AI Workbench kullanıcı yönetimli not defterleri (user-managed notebooks), BigQuery tablosundaki verilerin analiz edilmesi ve görselleştirilmesi için daha fazla esneklik ve özelleştirme sağlar. Python kütüphaneleri (pandas, matplotlib, seaborn vb.) kullanarak, veri dağılımlarının görselleştirmelerini oluşturabilir ve daha karmaşık istatistiksel analizler gerçekleştirebilirsiniz.

I think it's A.One time report containing real datasets STATISTICAL measurements to tell if the data is suitable for model development. Target audience is also other ML engineers. Getting a whole report of exactly this with TFDV/Facets is like two lines of code: https://www.tensorflow.org/tfx/data_validation/get_started A similar data studio report for this would take lots of time and work, and there would be no benefit from reuseability since task was a one-time job.