VALID EXAM ASSOCIATE-DATA-PRACTITIONER BOOK, ASSOCIATE-DATA-PRACTITIONER LATEST STUDY MATERIALS

Valid Exam Associate-Data-Practitioner Book, Associate-Data-Practitioner Latest Study Materials

Valid Exam Associate-Data-Practitioner Book, Associate-Data-Practitioner Latest Study Materials

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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.

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Google Cloud Associate Data Practitioner Sample Questions (Q33-Q38):

NEW QUESTION # 33
You are responsible for managing Cloud Storage buckets for a research company. Your company has well-defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?

  • A. Configure the buckets to use the Autoclass feature.
  • B. Configure the buckets to use the Standard storage class and enable Object Versioning.
  • C. Configure the buckets to use the Archive storage class.
  • D. Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.

Answer: D

Explanation:
Configuring a lifecycle management policy on each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well-defined data tiering and retention needs, providing cost efficiency and automation.


NEW QUESTION # 34
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

  • A. Use authorized views to share query results with the payroll specialist.
  • B. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
  • C. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
  • D. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.

Answer: A

Explanation:
Using authorized views is the simplest and most secure way to grant the payroll specialist access to aggregated performance data without exposing the entire dataset. Authorized views allow you to create a view in BigQuery that contains only the query results for the aggregated performance data. The payroll specialist can query the view without being granted access to the underlying sensitive data. This approach ensures security, adheres to the principle of least privilege, and eliminates the need to manage complex row-level or column-level permissions.


NEW QUESTION # 35
Your retail company collects customer data from various sources:
You are designing a data pipeline to extract this dat
a. Which Google Cloud storage system(s) should you select for further analysis and ML model training?

  • A. 1. Online transactions: BigQuery
    2. Customer feedback: Cloud Storage
    3. Social media activity: BigQuery
  • B. 1. Online transactions: Bigtable
    2. Customer feedback: Cloud Storage
    3. Social media activity: CloudSQL for MySQL
  • C. 1. Online transactions: Cloud Storage
    2. Customer feedback: Cloud Storage
    3. Social media activity: Cloud Storage
  • D. 1. Online transactions: Cloud SQL for MySQL
    2. Customer feedback: BigQuery
    3. Social media activity: Cloud Storage

Answer: A

Explanation:
Online transactions: Storing the transactional data in BigQuery is ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback: Storing customer feedback in Cloud Storage is appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity: Storing real-time social media activity in BigQuery is optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.


NEW QUESTION # 36
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

  • A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
  • B. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.
  • C. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
  • D. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:
For a small dataset with simple transformations (filtering, aggregation), Google recommends leveraging BigQuery's native SQL capabilities to minimize cost and complexity.
* Option A: Dataproc with Spark is overkill for a small dataset, incurring cluster management costs and setup time.
* Option B: BigQuery can load data directly from Cloud Storage (e.g., CSV, JSON) and perform transformations using SQL in a serverless manner, avoiding additional service costs. This is the most efficient and cost-effective approach.
* Option C: Cloud Data Fusion is suited for complex ETL but adds overhead (instance setup, UI design) unnecessary for simple tasks.


NEW QUESTION # 37
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 TB of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

  • A. 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.
  • B. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move 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: A

Explanation:
Using Transfer Appliances is 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 # 38
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