ASSOCIATE-DATA-PRACTITIONER EXAM ACTUAL TESTS | ASSOCIATE-DATA-PRACTITIONER NEW PRACTICE QUESTIONS

Associate-Data-Practitioner Exam Actual Tests | Associate-Data-Practitioner New Practice Questions

Associate-Data-Practitioner Exam Actual Tests | Associate-Data-Practitioner New Practice Questions

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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 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 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

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Associate-Data-Practitioner New Practice Questions | Associate-Data-Practitioner Authorized Certification

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Google Cloud Associate Data Practitioner Sample Questions (Q43-Q48):

NEW QUESTION # 43
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?

  • A.
  • B.
  • C.
  • D.

Answer: C

Explanation:
To calculate the weekly moving average of sales by location:
* The query must group bystore_id(partitioning the calculation by each store).
* TheORDER BY dateensures the sales are evaluated chronologically.
* TheROWS BETWEEN 6 PRECEDING AND CURRENT ROWspecifies a rolling window of 7 rows (1 week if each row represents daily data).
* TheAVG(total_sales)computes the average sales over the defined rolling window.
Chosen querymeets these requirements:
PARTITION BY store_idgroups the calculation by each store.

ORDER BY dateorders the rows correctly for the rolling average.

ROWS BETWEEN 6 PRECEDING AND CURRENT ROWensures the 7-day moving average.

Extract from Google Documentation: From "Analytic Functions in BigQuery" (https://cloud.google.com
/bigquery/docs/reference/standard-sql/analytic-function-concepts):"Use ROWS BETWEEN n PRECEDING AND CURRENT ROW with ORDER BY a time column to compute moving averages over a fixed number of rows, such as a 7-day window, partitioned by a grouping key like store_id."


NEW QUESTION # 44
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_uype='logisric_reg') AS SELECT * from cusromer_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_type='logistic_reg*) as select ' except(churned) FROM customer 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 # 45
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. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery 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. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.

Answer: B

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 # 46
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

  • A. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
  • B. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.
  • C. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies.
    Set up scheduled alerts based when anomalies or inconsistencies occur.
  • D. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.

Answer: A

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Cloud Monitoring is the recommended service for monitoring Google Cloud services, including Dataflow.
It allows you to track key metrics like system lag, element throughput, and error rates.
Alerting policies in Cloud Monitoring can trigger notifications based on metric thresholds.
Why other options are incorrect:B: The Dataflow job monitoring interface is useful for visualization, but Cloud Monitoring provides more comprehensive alerting.
C: BigQuery is for analyzing the processed data, not monitoring the pipeline itself. Also Cloud Storage is not where the data resides during processing.
D: Cloud Logging is useful for viewing logs, but Cloud Monitoring is better for metric-based alerting.


NEW QUESTION # 47
Your company uses Looker to visualize and analyze sales data. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

  • A. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
  • B. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
  • C. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
  • D. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.

Answer: A

Explanation:
Creating asingle Explorewith all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.
Looker's recommended approach for dashboards is a single, unified Explore for scalability and usability, supporting filters and drill-downs.
* Option A: Materialized views in BigQuery optimize queries but bypass Looker's modeling layer, reducing flexibility.
* Option B: Custom visualizations are for specific rendering, not multi-metric dashboards with filtering
/drill-down.
* Option C: Multiple Explores fragment the data model, complicating dashboard cohesion and maintenance.


NEW QUESTION # 48
......

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