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Latest Associate-Data-Practitioner Mock Exam - Test Associate-Data-Practitioner Simulator Fee

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Google Associate-Data-Practitioner Exam Syllabus Topics:

Topic
Details

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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Google Cloud Associate Data Practitioner Sample Questions (Q38-Q43):

NEW QUESTION # 38
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables.Use the row_number() window function to query the new materialized view.
B. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
C. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
D. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Answer: D

Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Materialized views in BigQuery are precomputed views that periodically cache the results of a query. This ensures up-to-date results automatically.
A UNION is the correct operation to combine the data from multiple regional sales tables.
RANK() function is correct to rank the sales regions. ROW_NUMBER() would create a unique number for each row, even if sales amount is the same, this is not the desired function.
Why other options are incorrect:A and B: Standard tables do not provide automatic updates.
D: A CROSS JOIN would produce a Cartesian product, which is not appropriate for combining regional sales data.
Cross join is used when you want every combination of rows from tables, not a aggregation of data.

NEW QUESTION # 39
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A. Compare the confusion matrix.
B. Evaluate the data skewness.
C. Evaluate data drift.
D. Compare the two different models.

Answer: C

Explanation:
Evaluating data drift involves analyzing changes in the distribution of the current serving data compared to the historical data used to train the model. If significant drift is detected, it indicates that the data patterns have changed over time, which can impact the model's performance. This analysis helps determine whether retraining the model is necessary to ensure its predictions remain accurate and relevant. Data drift evaluation is a standard approach for monitoring machine learning models over time.

NEW QUESTION # 40
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. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: C

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 # 41
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the data. What should you do?

A. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
B. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
C. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
D. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.

Answer: B

Explanation:
UsingTransfer Appliancesis the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.

NEW QUESTION # 42
You are a database administrator managing sales transaction data by region stored in a BigQuery table. You need to ensure that each sales representative can only see the transactions in their region. What should you do?

A. Create a data masking rule.
B. Grant the appropriate 1AM permissions on the dataset.
C. Create a row-level access policy.
D. Add a policy tag in BigQuery.

Answer: C

NEW QUESTION # 43
......

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