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Snowflake DEA-C01 Exam Syllabus Topics:
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NEW QUESTION # 15
A data engineer creates an AWS Glue Data Catalog table by using an AWS Glue crawler that is named Orders. The data engineer wants to add the following new partitions:
s3://transactions/orders/order_date=2023-01-01
s3://transactions/orders/order_date=2023-01-02
The data engineer must edit the metadata to include the new partitions in the table without scanning all the folders and files in the location of the table.
Which data definition language (DDL) statement should the data engineer use in Amazon Athena?
- A. ALTER TABLE Orders ADD PARTITION(order_date='2023-01-01') LOCATION
's3://transactions/orders/order_date=2023-01-01';
ALTER TABLE Orders ADD PARTITION(order_date='2023-01-02') LOCATION
's3://transactions/orders/order_date=2023-01-02'; - B. REPAIR TABLE Orders;
- C. ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-01') LOCATION
's3://transactions/orders/2023-01-01';
ALTER TABLE Orders MODIFY PARTITION(order_date='2023-01-02') LOCATION
's3://transactions/orders/2023-01-02'; - D. MSCK REPAIR TABLE Orders;
Answer: A
Explanation:
https://docs.aws.amazon.com/athena/latest/ug/alter-table-add-partition.html
NEW QUESTION # 16
A data engineer at a company is optimizing extract, transform, and load (ETL) workflows. The current architecture uses Amazon EMR and Apache Spark for large-scale transformations and AWS Glue for other ETL tasks. The workflows load processed data into an Amazon S3 based data lake.
The company wants to move to a fully managed serverless solution that can orchestrate multiple ETL jobs and automate execution. The new solution must continue to use Spark to process data.
The company needs to orchestrate and automate the ETL workflows with minimal manual intervention.
Which solution will meet these requirements?
- A. Migrate all ETL jobs to AWS Glue. Use AWS Glue workflows to orchestrate the pipeline.
- B. Use Amazon Managed Workflows for Apache Airflow automatic scheduling to orchestrate the Spark-based ETL jobs.
- C. Configure AWS Lambda functions to process Amazon S3 event notifications for data transformation tasks when new data is uploaded.
- D. Configure AWS Step Functions and Amazon EventBridge to orchestrate and invoke ETL workflows in AWS Glue and Amazon EMR.
Answer: A
Explanation:
AWS Glue provides a fully managed serverless ETL service with native Spark support. By migrating all ETL jobs to Glue, the company eliminates the need to manage Amazon EMR clusters. Glue workflows can orchestrate and automate multiple ETL jobs, ensuring dependencies are respected and executions are automated with minimal manual intervention. This satisfies the requirement for a serverless, Spark-based, fully managed solution.
NEW QUESTION # 17
For the most efficient and cost-effective Data load experience, Data Engineer needs to inconsider-ate which of the following considerations?
- A. When preparing your delimited text (CSV) files for loading, the number of columns in each row should be consistent.
- B. Amazon Kinesis Firehose can be convenient way to aggregate and batch data files which also allows defining both the desired file size, called the buffer size, and the wait interval after which a new file is sent, called the buffer interval.
- C. Split larger files into a greater number of smaller files, maximize the processing over-head for each file.
(Correct) - D. if the "null" values in your files indicate missing values and have no other special mean-ing, Snowflake recommend setting the file format option STRIP_NULL_VALUES to TRUE when loading the semi-structured data file.
- E. Enabling the STRIP_OUTER_ARRAY file format option for the COPY INTO <ta-ble> command to remove the outer array structure and load the records into separate table rows.
Answer: C
Explanation:
Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.
NEW QUESTION # 18
Select the incorrect statement while working with warehouses?
- A. Resizing a warehouse to a larger size is useful while loading and unloading significant amounts of data.
- B. Compute resources waiting to shut down are considered to be in "quiesce" mode.
- C. Resizing a suspended warehouse does not provision any new compute resources for the warehouse.
- D. Resizing a warehouse will have any immediate impact on statements that are currently being executed by the warehouse.
Answer: D
Explanation:
Explanation
Resizing a warehouse doesn't have any impact on statements that are currently being executed by the warehouse. When resizing to a larger size, the new compute resources, once fully provisioned, are used only to execute statements that are already in the warehouse queue, as well as all future statements submitted to the warehouse.
NEW QUESTION # 19
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVEcolumn for the North American region?
- A.
- B.
- C.
- D.
Answer: C
Explanation:
Explanation
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = 'North America'. Therefore, the function call in Option B will return the desired result.
NEW QUESTION # 20
What is the primary purpose of data lineage in data engineering?
- A. To transform data formats.
- B. To create visualizations.
- C. To trace the source and flow of data.
- D. To optimize query performance.
Answer: C
NEW QUESTION # 21
A sales company uses AWS Glue ETL to collect, process, and ingest data into an Amazon S3 bucket. The AWS Glue pipeline creates a new file in the S3 bucket every hour. File sizes vary from 200 KB to 300 KB. The company wants to build a sales prediction model by using data from the previous 5 years. The historic data includes 44,000 files.
The company builds a second AWS Glue ETL pipeline by using the smallest worker type. The second pipeline retrieves the historic files from the S3 bucket and processes the files for downstream analysis. The company notices significant performance issues with the second ETL pipeline.
The company needs to improve the performance of the second pipeline.
Which solution will meet this requirement MOST cost-effectively?
- A. Increase the number of workers in the AWS Glue ETL jobs.
- B. Use a larger worker type.
- C. Use the AWS Glue DynamicFrame grouping option.
- D. Enable AWS Glue auto scaling.
Answer: C
Explanation:
Using the AWS Glue DynamicFrame grouping option (for example, groupFiles=True with an appropriate groupSize) combines many small input files into larger partitions at read time. This reduces the per-file overhead of task initialization and metadata operations, yielding much faster ETL runs without the added cost of more or bigger workers.
NEW QUESTION # 22
A data engineer is processing and analyzing multiple terabytes of raw data that is in Amazon S3.
The data engineer needs to clean and prepare the data. Then the data engineer needs to load the data into Amazon Redshift for analytics.
The data engineer needs a solution that will give data analysts the ability to perform complex queries. The solution must eliminate the need to perform complex extract, transform, and load (ETL) processes or to manage infrastructure.
Which solution will meet these requirements with the LEAST operational overhead?
- A. Use AWS Lambda to prepare the data. Use Amazon Kinesis Data Firehose to load the data into Amazon Redshift. Use Amazon Athena to run queries.
- B. Use AWS Glue DataBrew to prepare the data. Use AWS Glue to load the data into Amazon Redshift. Use Amazon Redshift to run queries.
- C. Use AWS Glue to prepare the data. Use AWS Database Migration Service (AVVS DMS) to load the data into Amazon Redshift. Use Amazon Redshift Spectrum to run queries.
- D. Use Amazon EMR to prepare the data. Use AWS Step Functions to load the data into Amazon Redshift. Use Amazon QuickSight to run queries.
Answer: B
NEW QUESTION # 23
An insurance company stores transaction data that the company compressed with gzip.
The company needs to query the transaction data for occasional audits.
Which solution will meet this requirement in the MOST cost-effective way?
- A. Store the data in Amazon S3. Use Amazon Athena to query the data.
- B. Store the data in Amazon S3. Use Amazon S3 Select to query the data.
- C. Store the data in Amazon Glacier Flexible Retrieval. Use Amazon S3 Glacier Select to query the data.
- D. Store the data in Amazon Glacier Instant Retrieval. Use Amazon Athena to query the data.
Answer: C
NEW QUESTION # 24
A marketing company uses Amazon S3 to store marketing data. The company uses versioning in some buckets. The company runs several jobs to read and load data into the buckets.
To help cost-optimize its storage, the company wants to gather information about incomplete multipart uploads and outdated versions that are present in the S3 buckets.
Which solution will meet these requirements with the LEAST operational effort?
- A. Use Amazon S3 Inventory configurations reports to gather the information.
- B. Use the Amazon S3 Storage Lens dashboard to gather the information.
- C. Use AWS usage reports for Amazon S3 to gather the information.
- D. Use AWS CLI to gather the information.
Answer: B
Explanation:
Amazon S3 Storage Lens provides a comprehensive view of S3 storage usage and activity. It includes metrics such as the number of incomplete multipart uploads and the number of non- current object versions, making it an ideal tool for gathering insights into storage inefficiencies like outdated versions and incomplete uploads. This solution requires minimal operational effort as it is built into S3 and provides a user-friendly dashboard with detailed insights.
Using the AWS CLI to manually list and check multipart uploads and object versions would require scripting and regular execution, leading to higher operational overhead.
S3 Inventory provides reports on the objects in your bucket, but it does not directly provide information on incomplete multipart uploads or outdated versions. You would need to process the data further to gather the specific insights.
AWS usage reports are more focused on billing and usage rather than detailed storage optimization metrics like incomplete multipart uploads and outdated object versions.
NEW QUESTION # 25
Stuart, a Lead Data Engineer in MACRO Data Company created streams on set of External tables. He has been asked to extend the data retention period of the stream for 90 days, which parameter he can utilize to enable this extension?
- A. None of the above
- B. DATA_EXTENSION_TIME_IN_DAYS
- C. DATA_RETENTION_TIME_IN_DAYS
- D. MAX_DATA_EXTENSION_TIME_IN_DAYS
Answer: A
Explanation:
Explanation
External tables do not have data retention period applicable.
Good to Understand other Options available.
DATA_RETENTION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Number of days for which Snowflake retains historical data for performing Time Trav-el actions (SELECT, CLONE, UNDROP) on the object. A value of 0 effectively disables Time Travel for the specified database, schema, or table.
Values:
0 or 1 (for Standard Edition)
0 to 90 (for Enterprise Edition or higher)
Default:
1
MAX_DATA_EXTENSION_TIME_IN_DAYS
Type: Object (for databases, schemas, and tables) - Can be set for Account - Database - Schema - Table Description: Maximum number of days for which Snowflake can extend the data retention period for tables to prevent streams on the tables from becoming stale. By default, if the DA-TA_RETENTION_TIME_IN_DAYS setting for a source table is less than 14 days, and a stream has not been consumed, Snowflake temporarily extends this period to the stream's offset, up to a maximum of 14 days, regardless of the Snowflake Edition for your account. The MAX_DATA_EXTENSION_TIME_IN_DAYS parameter enables you to limit this automatic ex-tension period to control storage costs for data retention or for compliance reasons.
This parameter can be set at the account, database, schema, and table levels. Note that setting the parameter at the account or schema level only affects tables for which the parameter has not already been explicitly set at a lower level (e.g. at the table level by the table owner). A value of 0 effective-ly disables the automatic extension for the specified database, schema, or table.
Values:
0 to 90 (i.e. 90 days) - a value of 0 disables the automatic extension of the data retention period. To increase the maximum value for tables in your account, Client needs to contact Snowflake Sup-port.
Default:14
NEW QUESTION # 26
Which are the Cloud Platforms that Support Calling an External Function?
- A. AWS only
- B. GCP
- C. AWS,GCP,AZURE
- D. AWS & AZURE
Answer: C
NEW QUESTION # 27
A data engineer needs to optimize the performance of a data pipeline that handles retail orders.
Data about the orders is ingested daily into an Amazon S3 bucket.
The data engineer runs queries once each week to extract metrics from the orders data based the order date for multiple date ranges. The data engineer needs an optimization solution that ensures the query performance will not degrade when the volume of data increases.
Which solution will meet this requirement MOST cost-effectively?
- A. Partition the data based on load date. Use Amazon EMR to query the data.
- B. Partition the data based on load date. Use Amazon Aurora to query the data.
- C. Partition the data based on order date. Use Amazon Redshift to query the data.
- D. Partition the data based on order date. Use Amazon Athena to query the data.
Answer: D
Explanation:
Partitioning the S3 data by the order date lets Athena prune scans to only the relevant date folders, keeping query times stable as data grows. Because Athena is a serverless, pay-per- query service, you only pay for the data scanned, making it the most cost-effective way to run your weekly date-range metrics.
NEW QUESTION # 28
A company that operates globally must follow regulations that require data from an AWS Region to be accessible only within that Region.
A data engineer is creating a data pipeline that will create resources in the Region where the data engineer works. The data pipeline should have access to data only from the Region where the data engineer works. The pipeline uses Active Directory as an identity and authentication system.
The pipeline uses a custom identity broker application to verify that employees are signed in to Active Directory and to obtain temporary credentials by using the AssumeRole API operation.
Which solution will meet the locality requirements with the LEAST administrative effort?
- A. Create an IAM group for each Region. Include the required IAM policies for each IAM group. Add users to each IAM group so that when users log in by obtaining the temporary credentials, the users will receive the appropriate access based on the IAM group.
- B. Create individual IAM policies that allow users to create resources in a specific Region. Assign the policies to each data engineer. Allow users to assume the individually assigned role when the users log in to AWS.
- C. Create an IAM role for data engineers in each Region separately. Instruct each data engineer to obtain temporary credentials by assuming the appropriate Region specific IAM role.
- D. Create an IAM role that has permissions to create resources. Create a policy for each Region that ensures users can create resources only in that Region. Pass the policy as the session policy when employees obtain the temporary credentials.
Answer: D
Explanation:
Using a single IAM role with region-scoped session policies (using the aws:RequestedRegion condition) applied at AssumeRole time lets the identity broker grant temporary credentials limited to the engineer's Region. This enforces Regional access while avoiding per-Region roles or per- user policies, minimizing administration.
NEW QUESTION # 29
A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?
- A. Specify a combination of distribution, sort, and partition keys for all tables.
- B. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
- C. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
- D. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
Answer: C
Explanation:
Use the ALL Distribution Style for Rarely Updated Small Tables. This approach optimizes the performance of joins involving these smaller tables and is a common best practice in Redshift data warehousing. For the larger tables, maintaining the EVEN distribution style or considering a KEY-based distribution (if there are common join columns) could be more appropriate.
NEW QUESTION # 30
A company has an application that uses an Amazon API Gateway REST API and an AWS Lambda function to retrieve data from an Amazon DynamoDB instance. Users recently reported intermittent high latency in the application's response times. A data engineer finds that the Lambda function experiences frequent throttling when the company's other Lambda functions experience increased invocations.
The company wants to ensure the API's Lambda function operate without being affected by other Lambda functions.
Which solution will meet this requirement MOST cost-effectively?
- A. Increase the Lambda function timeout and allocated memory.
- B. Configure provisioned concurrency for the Lambda function.
- C. Configure reserved concurrency for the Lambda function.
- D. Increase the number of read capacity unit (RCU) in DynamoDB.
Answer: C
Explanation:
Reserved concurrency ensures that a specific Lambda function has a set number of concurrent executions that cannot be throttled due to other Lambda functions consuming shared concurrency limits. By configuring reserved concurrency, the API's Lambda function will always have the necessary resources to execute without being affected by other functions, thereby preventing throttling and ensuring consistent performance.
NEW QUESTION # 31
A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?
- A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the events to the logs S3 bucket.
- B. Create a trail of management events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
- C. Create a trail of data events in AWS CloudTraiL. Configure the trail to receive data from the transactions S3 bucket. Specify an empty prefix and write-only events. Specify the logs S3 bucket as the destination bucket.
- D. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda function. Program the Lambda function to write the event to Amazon Kinesis Data Firehose. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
Answer: C
Explanation:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/logging-with-S3.html
NEW QUESTION # 32
A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?
- A. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queue. Configure the SQS queue to invoke the Lambda function.
- B. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.
- C. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
- D. Use the Amazon Redshift Data API to publish an event to Amazon EventBridge. Configure an EventBridge rule to invoke the Lambda function.
Answer: D
Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api-monitoring-events.html
NEW QUESTION # 33
Ira a Data Engineer with TESLA IT systems, looking out to Compare Traditional Partitioning vs Snowflake micro-partitions for one of the Snowflake Project implementations. Which one of the following is incorrect understanding of Ira about Micro Partitioning?
- A. Snowflake stores metadata about all rows stored in a micro-partition, including number of distinct columns.
- B. In Snowflake, as data is inserted/loaded into a table, clustering metadata is collected and recorded for each micro-partition created during the process.
- C. All DML operations (e.g. DELETE, UPDATE, MERGE) take advantage of the under-lying micro-partition metadata to facilitate and simplify table maintenance.
- D. The micro-partition metadata maintained by Snowflake enables precise pruning of col-umns in micro-partitions at query run-time, including columns containing semi-structured data.
- E. All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage compared to traditional partitioning where specialized DDL required.
Answer: A
Explanation:
Explanation
What are Micro-partitions?
All data in Snowflake tables is automatically divided into micro-partitions, which are contiguous units of storage. Each micro-partition contains between 50 MB and 500 MB of uncompressed data (note that the actual size in Snowflake is smaller because data is always stored compressed). Groups of rows in tables are mapped into individual micro-partitions, organized in a columnar fashion. This size and structure allow for extremely granular pruning of very large tables, which can be comprised of millions, or even hundreds of millions, of micro-partitions.
Snowflake stores metadata about all rows stored in a micro-partition, including:
The range of values for each of the columns in the micro-partition.
The number of distinct values.
Additional properties used for both optimization and efficient query processing.
It Never stores number of columns as part of Metadata.
Rest of the statements are correct.
NEW QUESTION # 34
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