Exam Professional Cloud Architect topic 1 question 48 discussion

Answer is B: Keynotes from question: 1- On-premise data sources 2- Unfit data; not well maintained and degraded 3- Google-recommended best practice to "detect anomalies" <<-Very important. Explanation: A & C - incorrect; Datalab does not provide anomaly detection OOTB. It is used more for data science scenarios like interactive data analysis and build ML models. B - CORRECT; DataPrep OOTB provides for fast exploration and anomaly detection and lists cloud storage as an ingestion medium. Refer to ELT pipeline architecture here = https://cloud.google.com/dataprep D - incorrect; At this time DataPrep cannot connect to SaaS or on-premise source. Not to be confused for DataFlow which can!

Both B and D work, because the question says "Google's Best Practices" uploading the files first would keep the original copies Google encrypted and stored.

you have to bring data to Cloud storage before running data prep.

Why Cloud Storage is important ? 1. Centralized repository: Cloud Storage provides a secure and scalable place to store your data. This makes it accessible to various GCP services. 2. Data lake concept: This aligns with the idea of a data lake, where you bring raw data into a central location before processing and refining it. Why Cloud Dataprep is a good fit ? 1. Visual data exploration: Dataprep excels at helping you quickly understand your data through visualizations and profiling. This is crucial for identifying anomalies. 2. Data cleaning and transformation: Dataprep makes it easy to clean and standardize your data, which is essential before anomaly detection. Inconsistent formats, missing values, and errors can skew your analysis. 3. Built-in anomaly detection: Dataprep has features specifically designed to help you find anomalies. It can highlight unusual values, outliers, and patterns.

Datalab was shutdown. Its replacement is vertex AI. Read question accordingly

Cloud Datalab is a powerful interactive tool created to explore, analyze, transform, and visualize data and build machine learning models on Google Cloud Platform. Dataprep by Trifacta is an intelligent data service for visually exploring, cleaning, and preparing structured and unstructured data for analysis, reporting, and machine learning. Dataprep do not have an integration for on-prem: https://console.cloud.google.com/marketplace/product/endpoints/cloud-dataprep-editions-v2?project=fast-art-401415 So, clearly, the only option left is B.

B is correct.

Today, data ingestion to DataPrep can be Application, file upload, database. so B is also now valid

The recommended approach for detecting anomalies in your company data using Google-recommended practices is option B: Upload your files into Cloud Storage. Use Cloud Dataprep to explore and clean your data. Cloud Storage is a highly scalable, durable, and secure object storage service that can be used to store and retrieve data from anywhere on the web. You can use Cloud Storage to store your company data files and make them available for analysis. Cloud Dataprep is a fully managed data preparation service that allows you to quickly and easily explore, clean, and transform your data for analysis. It can help you detect anomalies in your data by providing features such as data profiling, data cleansing, and data transformation.

ok for B & D, but B is suitable to gcp

Datalab is deprecated : https://cloud.google.com/datalab/docs New Cloud Dataprep options will give connectivity to relational databases, business applications and extend our integrations across Google Cloud with Google Sheets: https://www.trifacta.com/blog/cloud-dataprep-trifacta/

ok for B

Could anyone provide a link where it explicitly says that Datprep does not connect to on-premises data sources. In the ingestion layer on the diagram at https://cloud.google.com/dataprep it shows databases as a source. I can't see anywhere that there is a limitation connecting to on-premises. Would be great if someone could share that.

It's gotta be B.

Keyword : Anamolies Data prep is the only product ... So options A and C is eliminated ... Cost effective is storing the data in GCS Cloud storage ... So option is B

Dataprep to detect anomalies in Data is the right choice.

B...It supports only CloudStorage and Bigquery..."So you can start transforming datasets, you hereby instruct Google to allow Trifacta, who provides the service Dataprep in collaboration with Google, to view and modify project data in Cloud Storage and BigQuery, run Dataflow jobs, and use all project service accounts."