Google Associate-Data-Practitioner Exam Dumps

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Why B is correct:Creating parallel transfer jobs by using include and exclude prefixes allows you to split the data into smaller chunks and transfer them in parallel.

This can significantly increase throughput and reduce the overall transfer time.

Why other options are incorrect:A: Changing the storage class to Standard will not improve transfer speed.

C: Dataflow is a complex solution for a simple file transfer task.

D: Agent-based transfer is suitable for large files or network limitations, but not for a large number of small files.

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Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let's analyze:

Option A: BigQuery ML's time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.

Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML's in-place processing.

Option C: Linear regression predicts continuous values but doesn't handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.

Option D: Logistic regression is for binary classification (e.g., yes/no), not time series forecasting of demand quantities. Why A is Best: ARIMA_PLUS in BigQuery ML automatically models seasonality and trends, requiring only SQL knowledge. It's serverless, scales with BigQuery's capacity, and keeps data in one place, minimizing complexity and cost. For example, CREATE MODEL ... OPTIONS(model_type='ARIMA_PLUS') followed by ML.FORECAST delivers accurate, scalable forecasts. Extract from Google Documentation: From 'BigQuery ML Time Series Forecasting' (https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series): 'The ARIMA_PLUS model type in BigQuery ML is designed for time series forecasting, accounting for seasonality and trends, making it ideal for predicting future values like product demand based on historical data.' Reference: Google Cloud Documentation - 'BigQuery ML Time Series' (https://cloud.google.com/bigquery-ml/docs/time-series).

Why A is Best: ARIMA_PLUS in BigQuery ML automatically models seasonality and trends, requiring only SQL knowledge. It's serverless, scales with BigQuery's capacity, and keeps data in one place, minimizing complexity and cost. For example, CREATE MODEL ... OPTIONS(model_type='ARIMA_PLUS') followed by ML.FORECAST delivers accurate, scalable forecasts.

Extract from Google Documentation: From 'BigQuery ML Time Series Forecasting' (https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series): 'The ARIMA_PLUS model type in BigQuery ML is designed for time series forecasting, accounting for seasonality and trends, making it ideal for predicting future values like product demand based on historical data.'

Option D: Logistic regression is for binary classification (e.g., yes/no), not time series forecasting of demand quantities. Why A is Best: ARIMA_PLUS in BigQuery ML automatically models seasonality and trends, requiring only SQL knowledge. It's serverless, scales with BigQuery's capacity, and keeps data in one place, minimizing complexity and cost. For example, CREATE MODEL ... OPTIONS(model_type='ARIMA_PLUS') followed by ML.FORECAST delivers accurate, scalable forecasts. Extract from Google Documentation: From 'BigQuery ML Time Series Forecasting' (https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create-time-series): 'The ARIMA_PLUS model type in BigQuery ML is designed for time series forecasting, accounting for seasonality and trends, making it ideal for predicting future values like product demand based on historical data.' Reference: Google Cloud Documentation - 'BigQuery ML Time Series' (https://cloud.google.com/bigquery-ml/docs/time-series).

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Why C is correct:Setting the Shared folder to 'View' ensures everyone can see the content.

Creating Looker groups simplifies access management.

Subfolders allow granular permissions for each team.

Granting 'Manage Access, Edit' allows teams to modify only their own content.

Why other options are incorrect:A: Grants View access only, so teams can't edit.

B: Moving content to personal folders defeats the purpose of sharing.

D: Grants edit access to all members of the team, not the team as a whole, which is not ideal.

Looker Access Control: https://cloud.google.com/looker/docs/access-control

Looker Groups: https://cloud.google.com/looker/docs/groups

Enabling automatic backups and creating a read replica of the Cloud SQL instance is the best solution to improve read performance. Read replicas allow you to offload read traffic from the primary database instance, reducing its load and improving overall performance. This approach is cost-effective and easy to implement within Cloud SQL. It ensures that the primary instance focuses on write operations while replicas handle read queries, providing a seamless performance boost with minimal effort.

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The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let's dive in:

Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn't fully serverless (requires instance management). It lacks native source code control and parameterization.

Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality---all meeting the requirements cost-effectively.

Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.

Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It's managed but not optimized for ELT within BigQuery alone. Why B is Best: Dataform leverages your team's SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT 'prod';), and quality checks (e.g., assert 'no_nulls' AS SELECT COUNT(*) FROM table WHERE col IS NULL). It's the perfect fit. Extract from Google Documentation: From 'Dataform Overview' (https://cloud.google.com/dataform/docs): 'Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management.' Reference: Google Cloud Documentation - 'Dataform' (https://cloud.google.com/dataform).

Why B is Best: Dataform leverages your team's SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT 'prod';), and quality checks (e.g., assert 'no_nulls' AS SELECT COUNT(*) FROM table WHERE col IS NULL). It's the perfect fit.

Extract from Google Documentation: From 'Dataform Overview' (https://cloud.google.com/dataform/docs): 'Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management.'

Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It's managed but not optimized for ELT within BigQuery alone. Why B is Best: Dataform leverages your team's SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT 'prod';), and quality checks (e.g., assert 'no_nulls' AS SELECT COUNT(*) FROM table WHERE col IS NULL). It's the perfect fit. Extract from Google Documentation: From 'Dataform Overview' (https://cloud.google.com/dataform/docs): 'Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management.' Reference: Google Cloud Documentation - 'Dataform' (https://cloud.google.com/dataform).