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Associate-Data-Practitioner Training Materials & Associate-Data-Practitioner Certification Training & Associate-Data-Practitioner Exam Questions

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Google Cloud Associate Data Practitioner Sample Questions (Q91-Q96):

NEW QUESTION # 91
Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use?

Answer: A

Explanation:
To calculate the weekly moving average of sales by location:
The query must group by store_id (partitioning the calculation by each store).
The ORDER BY date ensures the sales are evaluated chronologically.
The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data).
The AVG(total_sales) computes the average sales over the defined rolling window.
Chosen query meets these requirements:

NEW QUESTION # 92
You are working on a project that requires analyzing daily social media data. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google- recommended approach. What should you do?

A. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Use Dataflow to transform the data and write the transformed data to BigQuery.
C. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
D. Use Cloud Run functions to transform and load the data into BigQuery.

Answer: B

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Dataflow is a fully managed service for transforming and enriching data in both batch and streaming modes.
Dataflow is googles recomended way to transform large datasets.
It is designed for parallel processing, making it suitable for large datasets.
Why other options are incorrect:A: Manual downloading and scripting is not scalable or efficient.
B: Cloud Run functions are for stateless applications, not large data transformations.
D: While Cloud Data fusion could work, Dataflow is more optimized for large scale data transformation.

NEW QUESTION # 93
You manage a web application that stores data in a Cloud SQL database. You need to improve the read performance of the application by offloading read traffic from the primary database instance. You want to implement a solution that minimizes effort and cost. What should you do?

A. Store frequently accessed data in a Memorystore instance.
B. Enable automatic backups, and create a read replica of the Cloud SQL instance.
C. Use Cloud CDN to cache frequently accessed data.
D. Migrate the database to a larger Cloud SQL instance.

Answer: B

Explanation:
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.

NEW QUESTION # 94
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on- premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
B. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
C. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
D. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

Answer: A

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:Database Migration Service (DMS) is designed for migrating databases to Cloud SQL with minimal downtime and administrative overhead.
Cloud SQL for MySQL is a fully managed MySQL service, which aligns with the requirement to minimize administrative overhead.
Why other options are incorrect:A: Installing MySQL on Compute Engine requires manual management of the database instance, which increases administrative overhead.
C: BigQuery is not a direct replacement for a relational MySQL database. It's an analytical data warehouse.
D: Spanner is a globally distributed, scalable database, but it requires schema conversion and is not a direct replacement for MySQL, and it is also much more complex than cloud SQL.

NEW QUESTION # 95
You want to build a model to predict the likelihood of a customer clicking on an online advertisement. You have historical data in BigQuery that includes features such as user demographics, ad placement,and previous click behavior. After training the model, you want to generate predictions on new data. Which model type should you use in BigQuery ML?

A. Matrix factorization
B. Logistic regression
C. Linear regression
D. K-means clustering

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Predicting the likelihood of a click (binary outcome: click or no-click) requires a classification model.
BigQuery ML supports this use case with logistic regression.
* Option A: Linear regression predicts continuous values, not probabilities for binary outcomes.
* Option B: Matrix factorization is for recommendation systems, not binary prediction.
* Option C: Logistic regression predicts probabilities for binary classification (e.g., click likelihood), ideal for this scenario and supported in BigQuery ML.

NEW QUESTION # 96
......

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