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[General] Exam Associate-Data-Practitioner Fee, Associate-Data-Practitioner Pass Guarantee

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【General】 Exam Associate-Data-Practitioner Fee, Associate-Data-Practitioner Pass Guarantee

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Google Associate-Data-Practitioner Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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
Topic 3
  • 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.

Google Cloud Associate Data Practitioner Sample Questions (Q59-Q64):NEW QUESTION # 59
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 type='logistic_reg') AS select churned as label FROM customer_data;
  • C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
  • D. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
Answer: C
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 # 60
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?
  • A. Store the data in Cloud Storage using Archive storage.
  • B. Store the data in Cloud Storage using Standard storage.
  • C. Store the data in Cloud Storaqe usinq Coldline storaqe.
  • D. Store the data in Cloud Storaqe usinq Nearline storaqe.
Answer: D
Explanation:
Using Nearline storage in Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.

NEW QUESTION # 61
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
  • A. ETL
  • B. EL
  • C. ETLT
  • D. ELT
Answer: A
Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.

NEW QUESTION # 62
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. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
  • B. 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.
  • C. 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.
  • 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 # 63
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?
  • A. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
  • B. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
  • C. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
  • D. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
Answer: D
Explanation:
Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.

NEW QUESTION # 64
......
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