Google Professional Data Engineer Practice Test - Questions Answers, Page 28

A Reshuffle operation is a way to force Dataflow to split the pipeline into multiple stages, which can help isolate the performance of each step and identify bottlenecks. By monitoring the execution details in the Dataflow console, you can see the time, CPU, memory, and disk usage of each stage, as well as the number of elements and bytes processed. This can help you diagnose where the pipeline is slowing down and optimize it accordingly.Reference:

1: Reshuffling your data

2: Monitoring pipeline performance using the Dataflow monitoring interface

3: Optimizing pipeline performance

Implementing a data mesh approach involves treating data as a product and enabling decentralized data ownership and architecture. The steps outlined in option C support this approach by creating separate projects for each department, which aligns with the principle of domain-oriented decentralized data ownership. By allowing departments to create their own Cloud Storage buckets and BigQuery datasets, it promotes autonomy and self-service. Publishing the data in Analytics Hub facilitates data sharing and discovery across departments, enabling a collaborative environment where data can be easily accessed and utilized by different parts of the organization.

Architecture and functions in a data mesh - Google Cloud

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To store your data in a way that meets both access patterns, you should:

A) Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day.This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery's features such as partitioning, clustering, and caching.

B) Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key.This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs. BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness. Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.

C) Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day.This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.

D) Store your data in BigQuery. Use the metric as a primary key.This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.

Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure. The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep.Reference:

Dataprep overview

Dataprep features

Dataprep and BigQuery integration

BigQuery Omni is a multi-cloud analytics solution that lets you use the BigQuery interface to analyze data stored in other public clouds, such as AWS and Azure, without moving or copying the data. BigLake tables are a type of external table that let you query structured data in external data stores with access delegation. By using BigQuery Omni and BigLake tables, you can query data in AWS and Azure object stores directly from BigQuery, with minimal data movement and consistent performance.Reference:

1: Introduction to BigLake tables

2: Deep dive on how BigLake accelerates query performance

3: BigQuery Omni and BigLake (Analytics Data Federation on GCP)

For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.

Connected Sheets | Google Sheets | Google for Developers

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After processing your data, the next step in the model development lifecycle is to test and evaluate your model on the curated data. This is crucial to determine the performance of the model and to understand how well it can predict sales conversions for your customers. The evaluation phase involves using various metrics and techniques to assess the accuracy, precision, recall, and other relevant performance indicators of the model.It helps in identifying any issues or areas for improvement before deploying the model in a production environment.Reference: The information provided here is verified by the Google Professional Data Engineer Certification Exam Guide and related resources, which outline the steps and best practices in the model development lifecycle

Dataproc is a fully managed service that allows you to run Apache Hadoop and Spark workloads on Google Cloud. It is compatible with the open source ecosystem, so you can migrate your existing Hadoop clusters to Dataproc with minimal changes. Cloud Storage is a scalable, durable, and cost-effective object storage service that can replace HDFS for storing and accessing data. Cloud Storage offers interoperability with Hadoop through connectors, so you can use it as a data source or sink for your Dataproc jobs. Cloud Composer is a fully managed service that allows you to create, schedule, and monitor workflows using Apache Airflow. It is integrated with Google Cloud services, such as Dataproc, BigQuery, Dataflow, and Pub/Sub, so you can orchestrate your ETL pipelines across different platforms. Cloud Composer is compatible with your existing Airflow code, so you can migrate your existing orchestration processes to Cloud Composer with minimal changes.

The other options are not as suitable as Dataproc and Cloud Composer for this use case, because they either require more changes to your existing code, or do not meet your requirements. Dataflow is a fully managed service that allows you to create and run scalable data processing pipelines using Apache Beam. However, Dataflow is not compatible with your existing Hadoop code, so you would need to rewrite your ETL pipelines using Beam. Bigtable is a fully managed NoSQL database service that can handle large and complex data sets. However, Bigtable is not compatible with your existing Hadoop code, so you would need to rewrite your queries and applications using Bigtable APIs. Cloud Data Fusion is a fully managed service that allows you to visually design and deploy data integration pipelines using a graphical interface. However, Cloud Data Fusion is not compatible with your existing Airflow code, so you would need to recreate your orchestration processes using Cloud Data Fusion UI.Reference:

Dataproc overview

Cloud Storage connector for Hadoop

Cloud Composer overview