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Google Associate-Data-Practitioner New Practice Questions - Associate-Data-Practitioner Test Certification Cost

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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic
Details

Topic 1

Data Preparation and Ingestion: This section of the exam measures the skills of Google Cloud Engineers and covers the preparation and processing of data. Candidates will differentiate between various data manipulation methodologies such as ETL, ELT, and ETLT. They will choose appropriate data transfer tools, assess data quality, and conduct data cleaning using tools like Cloud Data Fusion and BigQuery. A key skill measured is effectively assessing data quality before ingestion.

Topic 2

Data Analysis and Presentation: This domain assesses the competencies of Data Analysts in identifying data trends, patterns, and insights using BigQuery and Jupyter notebooks. Candidates will define and execute SQL queries to generate reports and analyze data for business questions.| Data Pipeline Orchestration: This section targets Data Analysts and focuses on designing and implementing simple data pipelines. Candidates will select appropriate data transformation tools based on business needs and evaluate use cases for ELT versus ETL.

Topic 3

Data Management: This domain measures the skills of Google Database Administrators in configuring access control and governance. Candidates will establish principles of least privilege access using Identity and Access Management (IAM) and compare methods of access control for Cloud Storage. They will also configure lifecycle management rules to manage data retention effectively. A critical skill measured is ensuring proper access control to sensitive data within Google Cloud services

>> Google Associate-Data-Practitioner New Practice Questions <<

Associate-Data-Practitioner Test Certification Cost | Practice Test Associate-Data-Practitioner Pdf

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Google Cloud Associate Data Practitioner Sample Questions (Q46-Q51):

NEW QUESTION # 46
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
B. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
C. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
D. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

Answer: D

Explanation:
Using Dataflow to subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.

NEW QUESTION # 47
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rowsand transactional data. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost.
What should you do?

A. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
B. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
C. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
D. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.

Answer: D

Explanation:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.

NEW QUESTION # 48
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
B. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
D. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;

Answer: D

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:BigQuery ML requires the target label to be explicitly named label.
EXCEPT(churned) selects all columns except the churned column, which becomes the features.
churned AS label renames the churned column to label, which is required for BigQuery ML.
logistic_reg is the correct model_type option.
Why other options are incorrect:A: Does not rename the target column to label. Also has a typo in the model type.
C: Only selects the target label, not the features.
D: Has a syntax error with the single quote before except.

NEW QUESTION # 49
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
B. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
D. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.

Answer: D

Explanation:
Using a Cloud Run function triggered by Cloud Storage to load the data into BigQuery is the best solution because it minimizes both cost and maintenance while providing low-latency data ingestion. Cloud Run is a serverless platform that automatically scales based on the workload, ensuring efficient use of resources without requiring a dedicated instance or cluster. It integrates seamlessly with Cloud Storage event notifications, enabling real-time processing of incoming files and loading them into BigQuery. This approach is cost-effective, scalable, and easy to manage.

NEW QUESTION # 50
You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?

A. Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.
PREDICT function call to output the predictions into a new BigQuery table.
B. Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.
PREDICT function call to output the predictions into a new BigQuery table.
C. Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.
FORECAST function call to output the predictions into a new BigQuery table.
D. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:
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.

NEW QUESTION # 51
......

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