TEST ASSOCIATE-DATA-PRACTITIONER PASSING SCORE, GUARANTEED ASSOCIATE-DATA-PRACTITIONER QUESTIONS ANSWERS

Test Associate-Data-Practitioner Passing Score, Guaranteed Associate-Data-Practitioner Questions Answers

Test Associate-Data-Practitioner Passing Score, Guaranteed Associate-Data-Practitioner Questions Answers

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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 (Q104-Q109):

NEW QUESTION # 104
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

  • A. Create a saved query in the BigQuery console that uses the SUM( ) function and the DATE_SUB( ) function. Re-run the saved query every month, and save the results to a BigQuery table.
  • B. Create a materialized view in BigQuery that uses the SUM( ) function and the DATE_SUB( ) function.
  • C. Create a BigQuery table that uses the SUM( ) function and the DATE_DIFF( ) function.
  • D. Create a BigQuery table that uses the SUM( ) function and the _PARTITIONDATE filter.

Answer: B

Explanation:
Creating a materialized view in BigQuery with the SUM() function and the DATE_SUB() function is the best approach. Materialized views allow you to pre-aggregate and cache query results, making them efficient for repeated access, such as monthly reporting. By using the DATE_SUB() function, you can filter the inventory data to include only the most recent month. This approach ensures that the aggregation is up-to-date with minimal latency and provides efficient integration with Looker Studio for dashboarding.


NEW QUESTION # 105
Your company's ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?

  • A. Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.
  • B. Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.
  • C. Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.
  • D. Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U

Answer: B

Explanation:
Loading the data into BigQuery using a Cloud Run function and creating a BigQuery remote function that invokes the Cloud Translation API is a serverless and efficient approach. With this setup, you can use a scheduled query in BigQuery to invoke the remote function and translate new product reviews on a regular basis. This solution requires minimal maintenance, as BigQuery handles storage and querying, and the Cloud Translation API provides accurate translations without the need for custom ML model development.


NEW QUESTION # 106
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.
  • B.
  • C.
  • D.

Answer: B

Explanation:
In BigQuery ML, when creating a logistic regression model to predict customer churn, the correct query should:
Exclude the target label column (in this case, churned) from the feature columns, as it is used for training and not as a feature input.
Rename the target label column to label, as BigQuery ML requires the target column to be named label.
The chosen query satisfies these requirements:
SELECT * EXCEPT(churned), churned AS label: Excludes churned from features and renames it to label.
The OPTIONS(model_type='logistic_reg') specifies that a logistic regression model is being trained.
This setup ensures the model is correctly trained using the features in the dataset while targeting the churned column for predictions.


NEW QUESTION # 107
You manage a BigQuery table that is used for critical end-of-month reports. The table is updated weekly with new sales dat a. You want to prevent data loss and reporting issues if the table is accidentally deleted. What should you do?

  • A. Create a view of the table. On deletion, re-create the deleted table from the view and time travel data.
  • B. Configure the time travel duration on the table to be exactly seven days. On deletion, re-create the deleted table solely from the time travel data.
  • C. Schedule the creation of a new snapshot of the table once a week. On deletion, re-create the deleted table using the snapshot and time travel data.
  • D. Create a clone of the table. On deletion, re-create the deleted table by copying the content of the clone.

Answer: C

Explanation:
Scheduling the creation of a snapshot of the table weekly ensures that you have a point-in-time backup of the table. In case of accidental deletion, you can re-create the table from the snapshot. Additionally, BigQuery's time travel feature allows you to recover data from up to seven days prior to deletion. Combining snapshots with time travel provides a robust solution for preventing data loss and ensuring reporting continuity for critical tables. This approach minimizes risks while offering flexibility for recovery.


NEW QUESTION # 108
You need to create a data pipeline for a new application. Your application will stream data that needs to be enriched and cleaned. Eventually, the data will be used to train machine learning models. You need to determine the appropriate data manipulation methodology and which Google Cloud services to use in this pipeline. What should you choose?

  • A. ETL; Dataflow -> BigQuery
  • B. ELT; Cloud SQL -> Analytics Hub
  • C. ELT; Cloud Storage -> Bigtable
  • D. ETL; Cloud Data Fusion -> Cloud Storage

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
Streaming data requiring enrichment and cleaning before ML training suggests an ETL (Extract, Transform, Load) approach, with a focus on real-time processing and a data warehouse for ML.
* Option A: ETL with Dataflow (streaming transformations) and BigQuery (storage/ML training) is Google's recommended pattern for streaming pipelines. Dataflow handles enrichment/cleaning, and BigQuery supports ML model training (BigQuery ML).
* Option B: ETL with Cloud Data Fusion to Cloud Storage is batch-oriented and lacks streaming focus.
Cloud Storage isn't ideal for ML training directly.
* Option C: ELT (load then transform) with Cloud Storage to Bigtable is misaligned-Bigtable is for NoSQL, not ML training or post-load transformation.


NEW QUESTION # 109
......

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